The Biological Observation Matrix (BIOM) format¶
The BIOM file format (canonically pronounced biome) is designed to be a general-use format for representing biological sample by observation contingency tables. BIOM is a recognized standard for the Earth Microbiome Project and is a Genomics Standards Consortium supported project.
The BIOM format is designed for general use in broad areas of comparative -omics. For example, in marker-gene surveys, the primary use of this format is to represent OTU tables: the observations in this case are OTUs and the matrix contains counts corresponding to the number of times each OTU is observed in each sample. With respect to metagenome data, this format would be used to represent metagenome tables: the observations in this case might correspond to SEED subsystems, and the matrix would contain counts corresponding to the number of times each subsystem is observed in each metagenome. Similarly, with respect to genome data, this format may be used to represent a set of genomes: the observations in this case again might correspond to SEED subsystems, and the counts would correspond to the number of times each subsystem is observed in each genome.
There are two components to the BIOM project: first is the definition of the BIOM format, and second is development of support objects in multiple programming languages to support the use of BIOM in diverse bioinformatics applications. The version of the BIOM file format is independent of the version of the biom-format software.
There are official implementations of BIOM format support objects (APIs) in the Python and R programming languages. The rest of this site contains details about the BIOM file format (which is independent of the API) and the Python biom-format API. For more details about the R API, please see the CRAN biom package.
Projects using the BIOM format¶
If you are using BIOM in your project, and would like your project to be listed, please submit a pull request to the BIOM project. More information on submitting pull requests can be found here.
Contents¶
BIOM Documentation¶
These pages provide format specifications and API information for the BIOM table objects.
The biom file format¶
The BIOM project consists of two independent tools: the biom-format software package, which contains software tools for working with BIOM-formatted files and the tables they represent; and the BIOM file format. As of the 1.0.0 software version and the 1.0 file format version, the version of the software and the file format are independent of one another. Version specific documentation of the file formats can be found on the following pages.
The biom file format: Version 1.0¶
The biom format is based on JSON to provide the overall structure for the format. JSON is a widely supported format with native parsers available within many programming languages.
Required top-level fields:
id : <string or null> a field that can be used to id a table (or null)
format : <string> The name and version of the current biom format
format_url : <url> A string with a static URL providing format details
type : <string> Table type (a controlled vocabulary)
Acceptable values:
"OTU table"
"Pathway table"
"Function table"
"Ortholog table"
"Gene table"
"Metabolite table"
"Taxon table"
generated_by : <string> Package and revision that built the table
date : <datetime> Date the table was built (ISO 8601 format)
rows : <list of objects> An ORDERED list of obj describing the rows
(explained in detail below)
columns : <list of objects> An ORDERED list of obj describing the columns
(explained in detail below)
matrix_type : <string> Type of matrix data representation (a controlled vocabulary)
Acceptable values:
"sparse" : only non-zero values are specified
"dense" : every element must be specified
matrix_element_type : Value type in matrix (a controlled vocabulary)
Acceptable values:
"int" : integer
"float" : floating point
"unicode" : unicode string
shape : <list of ints>, the number of rows and number of columns in data
data : <list of lists>, counts of observations by sample
if matrix_type is "sparse", [[row, column, value],
[row, column, value],
...]
if matrix_type is "dense", [[value, value, value, ...],
[value, value, value, ...],
...]
Optional top-level fields:
comment : <string> A free text field containing any information that you
feel is relevant (or just feel like sharing)
The rows value is an ORDERED list of objects where each object corresponds to a single row in the matrix. Each object can currently store arbitrary keys, although this might become restricted based on table type. Each object must provide, at the minimum:
id : <string> an arbitrary UNIQUE identifier
metadata : <an object or null> A object containing key, value metadata pairs
The columns value is an ORDERED list of objects where each object corresponds to a single column in the matrix. Each object can currently store arbitrary keys, although this might become restricted based on table type. Each object must provide, at the minimum:
id : <string> an arbitrary UNIQUE identifier
metadata : <an object or null> A object containing key, value metadata pairs
Example biom files¶
Below are examples of minimal and rich biom files in both sparse and dense formats. To decide which of these you should generate for new data types, see the section on Tips and FAQs regarding the BIOM file format.
Minimal sparse OTU table¶
{
"id":null,
"format": "Biological Observation Matrix 0.9.1-dev",
"format_url": "http://biom-format.org/documentation/format_versions/biom-1.0.html",
"type": "OTU table",
"generated_by": "QIIME revision 1.4.0-dev",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":null},
{"id":"GG_OTU_2", "metadata":null},
{"id":"GG_OTU_3", "metadata":null},
{"id":"GG_OTU_4", "metadata":null},
{"id":"GG_OTU_5", "metadata":null}
],
"columns": [
{"id":"Sample1", "metadata":null},
{"id":"Sample2", "metadata":null},
{"id":"Sample3", "metadata":null},
{"id":"Sample4", "metadata":null},
{"id":"Sample5", "metadata":null},
{"id":"Sample6", "metadata":null}
],
"matrix_type": "sparse",
"matrix_element_type": "int",
"shape": [5, 6],
"data":[[0,2,1],
[1,0,5],
[1,1,1],
[1,3,2],
[1,4,3],
[1,5,1],
[2,2,1],
[2,3,4],
[2,4,2],
[3,0,2],
[3,1,1],
[3,2,1],
[3,5,1],
[4,1,1],
[4,2,1]
]
}
Minimal dense OTU table¶
{
"id":null,
"format": "Biological Observation Matrix 0.9.1-dev",
"format_url": "http://biom-format.org/documentation/format_versions/biom-1.0.html",
"type": "OTU table",
"generated_by": "QIIME revision 1.4.0-dev",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":null},
{"id":"GG_OTU_2", "metadata":null},
{"id":"GG_OTU_3", "metadata":null},
{"id":"GG_OTU_4", "metadata":null},
{"id":"GG_OTU_5", "metadata":null}
],
"columns": [
{"id":"Sample1", "metadata":null},
{"id":"Sample2", "metadata":null},
{"id":"Sample3", "metadata":null},
{"id":"Sample4", "metadata":null},
{"id":"Sample5", "metadata":null},
{"id":"Sample6", "metadata":null}
],
"matrix_type": "dense",
"matrix_element_type": "int",
"shape": [5,6],
"data": [[0,0,1,0,0,0],
[5,1,0,2,3,1],
[0,0,1,4,2,0],
[2,1,1,0,0,1],
[0,1,1,0,0,0]]
}
Rich sparse OTU table¶
{
"id":null,
"format": "Biological Observation Matrix 0.9.1-dev",
"format_url": "http://biom-format.org/documentation/format_versions/biom-1.0.html",
"type": "OTU table",
"generated_by": "QIIME revision 1.4.0-dev",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":{"taxonomy":["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}},
{"id":"GG_OTU_2", "metadata":{"taxonomy":["k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae", "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum", "s__"]}},
{"id":"GG_OTU_3", "metadata":{"taxonomy":["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__Methanosarcinaceae", "g__Methanosarcina", "s__"]}},
{"id":"GG_OTU_4", "metadata":{"taxonomy":["k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Halanaerobiales", "f__Halanaerobiaceae", "g__Halanaerobium", "s__Halanaerobiumsaccharolyticum"]}},
{"id":"GG_OTU_5", "metadata":{"taxonomy":["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}}
],
"columns":[
{"id":"Sample1", "metadata":{
"BarcodeSequence":"CGCTTATCGAGA",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample2", "metadata":{
"BarcodeSequence":"CATACCAGTAGC",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample3", "metadata":{
"BarcodeSequence":"CTCTCTACCTGT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample4", "metadata":{
"BarcodeSequence":"CTCTCGGCCTGT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}},
{"id":"Sample5", "metadata":{
"BarcodeSequence":"CTCTCTACCAAT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}},
{"id":"Sample6", "metadata":{
"BarcodeSequence":"CTAACTACCAAT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}}
],
"matrix_type": "sparse",
"matrix_element_type": "int",
"shape": [5, 6],
"data":[[0,2,1],
[1,0,5],
[1,1,1],
[1,3,2],
[1,4,3],
[1,5,1],
[2,2,1],
[2,3,4],
[2,5,2],
[3,0,2],
[3,1,1],
[3,2,1],
[3,5,1],
[4,1,1],
[4,2,1]
]
}
Rich dense OTU table¶
{
"id":null,
"format": "Biological Observation Matrix 0.9.1-dev",
"format_url": "http://biom-format.org/documentation/format_versions/biom-1.0.html",
"type": "OTU table",
"generated_by": "QIIME revision 1.4.0-dev",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":{"taxonomy":["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}},
{"id":"GG_OTU_2", "metadata":{"taxonomy":["k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae", "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum", "s__"]}},
{"id":"GG_OTU_3", "metadata":{"taxonomy":["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__Methanosarcinaceae", "g__Methanosarcina", "s__"]}},
{"id":"GG_OTU_4", "metadata":{"taxonomy":["k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Halanaerobiales", "f__Halanaerobiaceae", "g__Halanaerobium", "s__Halanaerobiumsaccharolyticum"]}},
{"id":"GG_OTU_5", "metadata":{"taxonomy":["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}}
],
"columns":[
{"id":"Sample1", "metadata":{
"BarcodeSequence":"CGCTTATCGAGA",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample2", "metadata":{
"BarcodeSequence":"CATACCAGTAGC",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample3", "metadata":{
"BarcodeSequence":"CTCTCTACCTGT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"gut",
"Description":"human gut"}},
{"id":"Sample4", "metadata":{
"BarcodeSequence":"CTCTCGGCCTGT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}},
{"id":"Sample5", "metadata":{
"BarcodeSequence":"CTCTCTACCAAT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}},
{"id":"Sample6", "metadata":{
"BarcodeSequence":"CTAACTACCAAT",
"LinkerPrimerSequence":"CATGCTGCCTCCCGTAGGAGT",
"BODY_SITE":"skin",
"Description":"human skin"}}
],
"matrix_type": "dense",
"matrix_element_type": "int",
"shape": [5,6],
"data": [[0,0,1,0,0,0],
[5,1,0,2,3,1],
[0,0,1,4,2,0],
[2,1,1,0,0,1],
[0,1,1,0,0,0]]
}
The biom file format: Version 2.0¶
The biom format is based on HDF5 to provide the overall structure for the format. HDF5 is a widely supported format with native parsers available within many programming languages.
Required top-level attributes:
id : <string or null> a field that can be used to id a table (or null)
format : <string> The name and version of the current biom format
format-url : <url> A string with a static URL providing format details
type : <string> Table type (a controlled vocabulary)
Acceptable values:
"OTU table"
"Pathway table"
"Function table"
"Ortholog table"
"Gene table"
"Metabolite table"
"Taxon table"
generated-by : <string> Package and revision that built the table
creation-date : <datetime> Date the table was built (ISO 8601 format)
nnz : <int> The number of non-zero elements in the table
shape : <list of ints>, the number of rows and number of columns in data
Required groups:
observation/ : The HDF5 group that contains observation specific information and an observation oriented view of the data
observation/matrix : The HDF5 group that contains matrix data oriented for observation-wise operations (e.g., in compressed sparse row format)
sample/ : The HDF5 group that contains sample specific information and a sample oriented data oriented view of the data
sample/matrix : The HDF5 group that contains matrix data oriented for sample-wise operations (e.g., in compressed sparse column format)
Required datasets:
observation/ids : <string> or <variable length string> A (N,) dataset of the observation IDs, where N is the total number of IDs
observation/matrix/data : <float64> A (nnz,) dataset containing the actual matrix data
observation/matrix/indices : <int32> A (nnz,) dataset containing the column indices (e.g., maps into samples/ids)
observation/matrix/indptr : <int32> A (M+1,) dataset containing the compressed row offsets
sample/ids : <string> or <variable length string> A (M,) dataset of the sample IDs, where M is the total number of IDs
sample/matrix/data : <float64> A (nnz,) dataset containing the actual matrix data
sample/matrix/indices : <int32> A (nnz,) dataset containing the row indices (e.g., maps into observation/ids)
sample/matrix/indptr : <int32> A (N+1,) dataset containing the compressed column offsets
Optional datasets:
observation/metadata : <variable length string or null> If specified, a (1,) dataset containing a JSON-string representation of the metadata
sample/metadata : <variable length string or null> If specified, a (1,) dataset containing a JSON-string representation of the metadata
The metadata for each axis (observation and sample) are described with JSON. The required structure, if the metadata are specified, is a list of objects, where the list is in index order with respect to the axis (e.g, the object at element 0 corresponds to ID 0 for the given axis). Any metadata that corresponds to the ID, such as taxonomy, can be represented in the object. For instance, the following JSON string describes taxonomy for three IDs:
Metadata description:
[
{"taxonomy": ["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}},
{"taxonomy": ["k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae", "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum", "s__"]}},
{"taxonomy": ["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__Methanosarcinaceae", "g__Methanosarcina", "s__"]}}
]
Example biom files¶
Below are examples of minimal and rich biom files in both sparse and dense formats. To decide which of these you should generate for new data types, see the section on Tips and FAQs regarding the BIOM file format.
BIOM 2.0 OTU table in the HDF5 data description langauge (DDL)¶
HDF5 "rich_sparse_otu_table_hdf5.biom" {
GROUP "/" {
ATTRIBUTE "creation-date" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "2014-05-13T14:50:32.052446"
}
}
ATTRIBUTE "format-url" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "http://biom-format.org"
}
}
ATTRIBUTE "format-version" {
DATATYPE H5T_STD_I64LE
DATASPACE SIMPLE { ( 2 ) / ( 2 ) }
DATA {
(0): 2, 0
}
}
ATTRIBUTE "generated-by" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "example"
}
}
ATTRIBUTE "id" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "No Table ID"
}
}
ATTRIBUTE "nnz" {
DATATYPE H5T_STD_I64LE
DATASPACE SCALAR
DATA {
(0): 15
}
}
ATTRIBUTE "shape" {
DATATYPE H5T_STD_I64LE
DATASPACE SIMPLE { ( 2 ) / ( 2 ) }
DATA {
(0): 5, 6
}
}
ATTRIBUTE "type" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "otu table"
}
}
GROUP "observation" {
DATASET "ids" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 5 ) / ( 5 ) }
DATA {
(0): "GG_OTU_1", "GG_OTU_2", "GG_OTU_3", "GG_OTU_4", "GG_OTU_5"
}
}
GROUP "matrix" {
DATASET "data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 1, 5, 1, 2, 3, 1, 1, 4, 2, 2, 1, 1, 1, 1, 1
}
}
DATASET "indices" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 2, 0, 1, 3, 4, 5, 2, 3, 5, 0, 1, 2, 5, 1, 2
}
}
DATASET "indptr" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): 0, 1, 6, 9, 13, 15
}
}
}
DATASET "metadata" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 1 ) / ( 1 ) }
DATA {
(0): "[{"taxonomy": ["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}, {"taxonomy": ["k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae", "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum", "s__"]}, {"taxonomy": ["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__Methanosarcinaceae", "g__Methanosarcina", "s__"]}, {"taxonomy": ["k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Halanaerobiales", "f__Halanaerobiaceae", "g__Halanaerobium", "s__Halanaerobiumsaccharolyticum"]}, {"taxonomy": ["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"]}]"
}
}
}
GROUP "sample" {
DATASET "ids" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "Sample1", "Sample2", "Sample3", "Sample4", "Sample5",
(5): "Sample6"
}
}
GROUP "matrix" {
DATASET "data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 5, 2, 1, 1, 1, 1, 1, 1, 1, 2, 4, 3, 1, 2, 1
}
}
DATASET "indices" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 1, 3, 1, 3, 4, 0, 2, 3, 4, 1, 2, 1, 1, 2, 3
}
}
DATASET "indptr" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 7 ) / ( 7 ) }
DATA {
(0): 0, 2, 5, 9, 11, 12, 15
}
}
}
DATASET "metadata" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 1 ) / ( 1 ) }
DATA {
(0): "[{"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CGCTTATCGAGA", "Description": "human gut", "BODY_SITE": "gut"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CATACCAGTAGC", "Description": "human gut", "BODY_SITE": "gut"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CTCTCTACCTGT", "Description": "human gut", "BODY_SITE": "gut"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CTCTCGGCCTGT", "Description": "human skin", "BODY_SITE": "skin"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CTCTCTACCAAT", "Description": "human skin", "BODY_SITE": "skin"}, {"LinkerPrimerSequence": "CATGCTGCCTCCCGTAGGAGT", "BarcodeSequence": "CTAACTACCAAT", "Description": "human skin", "BODY_SITE": "skin"}]"
}
}
}
}
}
The biom file format: Version 2.1¶
The biom format is based on HDF5 to provide the overall structure for the format. HDF5 is a widely supported format with native parsers available within many programming languages.
Required top-level attributes:
id : <string or null> a field that can be used to id a table (or null)
type : <string> Table type (a controlled vocabulary)
Acceptable values:
"OTU table"
"Pathway table"
"Function table"
"Ortholog table"
"Gene table"
"Metabolite table"
"Taxon table"
format-url : <url> A string with a static URL providing format details
format-version : <tuple> The version of the current biom format, major and minor
generated-by : <string> Package and revision that built the table
creation-date : <datetime> Date the table was built (ISO 8601 format)
shape : <list of ints>, the number of rows and number of columns in data
nnz : <int> The number of non-zero elements in the table
Required groups:
observation/ : The HDF5 group that contains observation specific information and an observation oriented view of the data
observation/matrix : The HDF5 group that contains matrix data oriented for observation-wise operations (e.g., in compressed sparse row format)
observation/metadata : The HDF5 group that contains observation specific metadata information
observation/group-metadata : The HDF5 group that contains observation specific group metadata information (e.g., phylogenetic tree)
sample/ : The HDF5 group that contains sample specific information and a sample oriented data oriented view of the data
sample/matrix : The HDF5 group that contains matrix data oriented for sample-wise operations (e.g., in compressed sparse column format)
sample/metadata : The HDF5 group that contains sample specific metadata information
sample/group-metadata : The HDF5 group that contains sample specific group metadata information (e.g., relationships between samples)
Required datasets:
observation/ids : <string> or <variable length string> A (N,) dataset of the observation IDs, where N is the total number of IDs
observation/matrix/data : <float64> A (nnz,) dataset containing the actual matrix data
observation/matrix/indices : <int32> A (nnz,) dataset containing the column indices (e.g., maps into samples/ids)
observation/matrix/indptr : <int32> A (M+1,) dataset containing the compressed row offsets
sample/ids : <string> or <variable length string> A (M,) dataset of the sample IDs, where M is the total number of IDs
sample/matrix/data : <float64> A (nnz,) dataset containing the actual matrix data
sample/matrix/indices : <int32> A (nnz,) dataset containing the row indices (e.g., maps into observation/ids)
sample/matrix/indptr : <int32> A (N+1,) dataset containing the compressed column offsets
Under the observation/metadata and sample/metadata groups, the user can specify an arbitrary number of datasets that represents a metadata category for that axis. The expected structure for each of these metadata datasets is a list of atomic type objects (int, float, str, ...) where the index order of the list corresponds to the index order of the relevant axis IDs. Special complex metadata fields have been defined, and they are stored in a specific way. Currently, the available special metadata fields are:
observation/metadata/taxonomy : <string> or <variable length string> A (N, ?) dataset containing the taxonomy names assigned to the observation
observation/metadata/KEGG_Pathways : <string> or <variable length string> A (N, ?) dataset containing the KEGG Pathways assigned to the observation
observation/metadata/collapsed_ids : <string> or <variable length string> A (N, ?) dataset containing the observation ids of the original table that have been collapsed in the given observation
sample/metadata/collapsed_ids : <string> or <variable length string> A (M, ?) dataset containing the sample ids of the original table that have been collapsed in the given sample
Under the observation/group-metadata and sample/group-metadata groups, the user can specify an arbitrary number of datasets that represents a relationship between the ids for that axis. The expected structure for each of these group metadata datasets is a single string or variable length string. Each of these datasets should have defined an attribute called data_type, which specifies how the string should be interpreted. One example of such group metadata dataset is observation/group-metadata/phylogeny, with the attribute observation/group-metadata/phylogeny.attrs['data_type'] = "newick", which stores a single string with the newick format of the phylogenetic tree for the observations.
Example biom files¶
Below is an examples of a rich biom file. To decide which of these you should generate for new data types, see the section on Tips and FAQs regarding the BIOM file format.
BIOM 2.1 OTU table in the HDF5 data description langauge (DDL)¶
HDF5 "examples/rich_sparse_otu_table_hdf5.biom" {
GROUP "/" {
ATTRIBUTE "creation-date" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "2014-07-29T16:16:36.617320"
}
}
ATTRIBUTE "format-url" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "http://biom-format.org"
}
}
ATTRIBUTE "format-version" {
DATATYPE H5T_STD_I64LE
DATASPACE SIMPLE { ( 2 ) / ( 2 ) }
DATA {
(0): 2, 1
}
}
ATTRIBUTE "generated-by" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "example"
}
}
ATTRIBUTE "id" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "No Table ID"
}
}
ATTRIBUTE "nnz" {
DATATYPE H5T_STD_I64LE
DATASPACE SCALAR
DATA {
(0): 15
}
}
ATTRIBUTE "shape" {
DATATYPE H5T_STD_I64LE
DATASPACE SIMPLE { ( 2 ) / ( 2 ) }
DATA {
(0): 5, 6
}
}
ATTRIBUTE "type" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "otu table"
}
}
GROUP "observation" {
GROUP "group-metadata" {
}
DATASET "ids" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 5 ) / ( 5 ) }
DATA {
(0): "GG_OTU_1", "GG_OTU_2", "GG_OTU_3", "GG_OTU_4", "GG_OTU_5"
}
}
GROUP "matrix" {
DATASET "data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 1, 5, 1, 2, 3, 1, 1, 4, 2, 2, 1, 1, 1, 1, 1
}
}
DATASET "indices" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 2, 0, 1, 3, 4, 5, 2, 3, 5, 0, 1, 2, 5, 1, 2
}
}
DATASET "indptr" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): 0, 1, 6, 9, 13, 15
}
}
}
GROUP "metadata" {
DATASET "taxonomy" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 5, 7 ) / ( 5, 7 ) }
DATA {
(0,0): "k__Bacteria", "p__Proteobacteria",
(0,2): "c__Gammaproteobacteria", "o__Enterobacteriales",
(0,4): "f__Enterobacteriaceae", "g__Escherichia", "s__",
(1,0): "k__Bacteria", "p__Cyanobacteria", "c__Nostocophycideae",
(1,3): "o__Nostocales", "f__Nostocaceae", "g__Dolichospermum",
(1,6): "s__",
(2,0): "k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia",
(2,3): "o__Methanosarcinales", "f__Methanosarcinaceae",
(2,5): "g__Methanosarcina", "s__",
(3,0): "k__Bacteria", "p__Firmicutes", "c__Clostridia",
(3,3): "o__Halanaerobiales", "f__Halanaerobiaceae",
(3,5): "g__Halanaerobium", "s__Halanaerobiumsaccharolyticum",
(4,0): "k__Bacteria", "p__Proteobacteria",
(4,2): "c__Gammaproteobacteria", "o__Enterobacteriales",
(4,4): "f__Enterobacteriaceae", "g__Escherichia", "s__"
}
}
}
}
GROUP "sample" {
GROUP "group-metadata" {
}
DATASET "ids" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_ASCII;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "Sample1", "Sample2", "Sample3", "Sample4", "Sample5",
(5): "Sample6"
}
}
GROUP "matrix" {
DATASET "data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 5, 2, 1, 1, 1, 1, 1, 1, 1, 2, 4, 3, 1, 2, 1
}
}
DATASET "indices" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 15 ) / ( 15 ) }
DATA {
(0): 1, 3, 1, 3, 4, 0, 2, 3, 4, 1, 2, 1, 1, 2, 3
}
}
DATASET "indptr" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 7 ) / ( 7 ) }
DATA {
(0): 0, 2, 5, 9, 11, 12, 15
}
}
}
GROUP "metadata" {
DATASET "BODY_SITE" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "gut", "gut", "gut", "skin", "skin", "skin"
}
}
DATASET "BarcodeSequence" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "CGCTTATCGAGA", "CATACCAGTAGC", "CTCTCTACCTGT",
(3): "CTCTCGGCCTGT", "CTCTCTACCAAT", "CTAACTACCAAT"
}
}
DATASET "Description" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "human gut", "human gut", "human gut", "human skin",
(4): "human skin", "human skin"
}
}
DATASET "LinkerPrimerSequence" {
DATATYPE H5T_STRING {
STRSIZE H5T_VARIABLE;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SIMPLE { ( 6 ) / ( 6 ) }
DATA {
(0): "CATGCTGCCTCCCGTAGGAGT", "CATGCTGCCTCCCGTAGGAGT",
(2): "CATGCTGCCTCCCGTAGGAGT", "CATGCTGCCTCCCGTAGGAGT",
(4): "CATGCTGCCTCCCGTAGGAGT", "CATGCTGCCTCCCGTAGGAGT"
}
}
}
}
}
}
Release versions contain three integers in the following format: major-version.minor-version.micro-version. When -dev is appended to the end of a version string that indicates a development (or between-release version). For example, 1.0.0-dev would refer to the development version following the 1.0.0 release.
Tips and FAQs regarding the BIOM file format¶
Motivation for the BIOM format¶
The BIOM format was motivated by several goals. First, to facilitate efficient handling and storage of large, sparse biological contingency tables; second, to support encapsulation of core study data (contingency table data and sample/observation metadata) in a single file; and third, to facilitate the use of these tables between tools that support this format (e.g., passing of data between QIIME, MG-RAST, and VAMPS.).
Efficient handling and storage of very large tables¶
In QIIME, we began hitting limitations with OTU table objects when working with thousands of samples and hundreds of thousands of OTUs. In the near future we expect that we’ll be dealing with hundreds of thousands of samples in single analyses.
The OTU table format up to QIIME 1.4.0 involved a dense matrix: if an OTU was not observed in a given sample, that would be indicated with a zero. We now primarily represent OTU tables in a sparse format: if an OTU is not observed in a sample, there is no count for that OTU. The two ways of representing this data are exemplified here.
A dense representation of an OTU table:
OTU ID PC.354 PC.355 PC.356
OTU0 0 0 4
OTU1 6 0 0
OTU2 1 0 7
OTU3 0 0 3
A sparse representation of an OTU table:
PC.354 OTU1 6
PC.354 OTU2 1
PC.356 OTU0 4
PC.356 OTU2 7
PC.356 OTU3 3
OTU table data tends to be sparse (e.g., greater than 90% of counts are zero, and frequently as many as 99% of counts are zero) in which case the latter format is more convenient to work with as it has a smaller memory footprint. Both of these representations are supported in the biom-format project via dense and sparse Table types. Generally if less than 85% of your counts are zero, a dense representation will be more efficient.
Encapsulation of core study data (OTU table data and sample/OTU metadata) in a single file¶
Formats, such as JSON and HDF5, made more efficient storage of highly sparse data and allowed for storage of arbitrary amounts of sample and OTU metadata in a single file. Sample metadata corresponds to what is generally found in QIIME mapping files. At this stage inclusion of this information in the OTU table file is optional, but it may be useful for sharing these files with other QIIME users and for publishing or archiving results of analyses. OTU metadata (generally a taxonomic assignment for an OTU) is also optional. In contrast to the previous OTU table format, you can now store more than one OTU metadata value in this field, so for example you can score taxonomic assignments based on two different taxonomic assignment approaches.
Facilitating the use of tables between tools that support this format¶
Different tools, such as QIIME, MG-RAST, and VAMPS work with similar data structures that represent different types of data. An example of this is a metagenome table that could be generated by MG-RAST (where for example, columns are metagenomes and rows are functional categories). Exporting this data from MG-RAST in a suitable format will allow for the application of many of the QIIME tools to this data (such as generation of alpha rarefaction plots or beta diversity ordination plots). This new format is far more general than previous formats, so will support adoption by groups working with different data types and is already being integrated to support transfer of data between QIIME, MG-RAST, and VAMPS.
File extension¶
We recommend that BIOM files use the .biom extension.
Quick start¶
BIOM has an example table and two methods for reading in Table objects that are immediately available at the package level.
Functions¶
Examples¶
Load an example table:
>>> from biom import example_table
>>> print example_table
# Constructed from biom file
#OTU ID S1 S2 S3
O1 0.0 1.0 2.0
O2 3.0 4.0 5.0
Parse a table from an open file object:
>>> from biom import parse_table
>>> with open('path/to/table.biom') as f:
... table = parse_table(f)
Parse a table from a path. BIOM will attempt to determine if the file is either in TSV, HDF5, JSON, gzip’d JSON or gzip’d TSV and parse accordingly:
>>> from biom import load_table
>>> table = load_table('path/to/table.biom')
BIOM Table (biom.table)¶
The biom-format project provides rich Table objects to support use of the
BIOM file format. The objects encapsulate matrix data (such as OTU counts) and
abstract the interaction away from the programmer.
Examples¶
First, lets create a toy table to play around with. For this example, we’re going to construct a 10x4 Table, or one that has 10 observations and 4 samples. Each observation and sample will be given an arbitrary but unique name. We’ll also add on some metadata.
>>> import numpy as np
>>> from biom.table import Table
>>> data = np.arange(40).reshape(10, 4)
>>> sample_ids = ['S%d' % i for i in range(4)]
>>> observ_ids = ['O%d' % i for i in range(10)]
>>> sample_metadata = [{'environment': 'A'}, {'environment': 'B'},
... {'environment': 'A'}, {'environment': 'B'}]
>>> observ_metadata = [{'taxonomy': ['Bacteria', 'Firmicutes']},
... {'taxonomy': ['Bacteria', 'Firmicutes']},
... {'taxonomy': ['Bacteria', 'Proteobacteria']},
... {'taxonomy': ['Bacteria', 'Proteobacteria']},
... {'taxonomy': ['Bacteria', 'Proteobacteria']},
... {'taxonomy': ['Bacteria', 'Bacteroidetes']},
... {'taxonomy': ['Bacteria', 'Bacteroidetes']},
... {'taxonomy': ['Bacteria', 'Firmicutes']},
... {'taxonomy': ['Bacteria', 'Firmicutes']},
... {'taxonomy': ['Bacteria', 'Firmicutes']}]
>>> table = Table(data, observ_ids, sample_ids, observ_metadata,
... sample_metadata, table_id='Example Table')
Now that we have a table, let’s explore it at a high level first.
>>> table
10 x 4 <class 'biom.table.Table'> with 39 nonzero entries (97% dense)
>>> print table
# Constructed from biom file
#OTU ID S0 S1 S2 S3
O0 0.0 1.0 2.0 3.0
O1 4.0 5.0 6.0 7.0
O2 8.0 9.0 10.0 11.0
O3 12.0 13.0 14.0 15.0
O4 16.0 17.0 18.0 19.0
O5 20.0 21.0 22.0 23.0
O6 24.0 25.0 26.0 27.0
O7 28.0 29.0 30.0 31.0
O8 32.0 33.0 34.0 35.0
O9 36.0 37.0 38.0 39.0
>>> print table.ids()
['S0' 'S1' 'S2' 'S3']
>>> print table.ids(axis='observation')
['O0' 'O1' 'O2' 'O3' 'O4' 'O5' 'O6' 'O7' 'O8' 'O9']
>>> print table.nnz # number of nonzero entries
39
While it’s fun to just poke at the table, let’s dig deeper. First, we’re going
to convert table into relative abundances (within each sample), and then
filter table to just the samples associated with environment ‘A’. The
filtering gets fancy: we can pass in an arbitrary function to determine what
samples we want to keep. This function must accept a sparse vector of values,
the corresponding ID and the corresponding metadata, and should return True
or False, where True indicates that the vector should be retained.
>>> normed = table.norm(axis='sample', inplace=False)
>>> filter_f = lambda values, id_, md: md['environment'] == 'A'
>>> env_a = normed.filter(filter_f, axis='sample', inplace=False)
>>> print env_a
# Constructed from biom file
#OTU ID S0 S2
O0 0.0 0.01
O1 0.0222222222222 0.03
O2 0.0444444444444 0.05
O3 0.0666666666667 0.07
O4 0.0888888888889 0.09
O5 0.111111111111 0.11
O6 0.133333333333 0.13
O7 0.155555555556 0.15
O8 0.177777777778 0.17
O9 0.2 0.19
But, what if we wanted individual tables per environment? While we could just perform some fancy iteration, we can instead just rely on Table.partition for these operations. partition, like filter, accepts a function. However, the partition method only passes the corresponding ID and metadata to the function. The function should return what partition the data are a part of. Within this example, we’re also going to sum up our tables over the partitioned samples. Please note that we’re using the original table (ie, not normalized) here.
>>> part_f = lambda id_, md: md['environment']
>>> env_tables = table.partition(part_f, axis='sample')
>>> for partition, env_table in env_tables:
... print partition, env_table.sum('sample')
A [ 180. 200.]
B [ 190. 210.]
For this last example, and to highlight a bit more functionality, we’re going to first transform the table such that all multiples of three will be retained, while all non-multiples of three will get set to zero. Following this, we’ll then collpase the table by taxonomy, and then convert the table into presence/absence data.
First, let’s setup the transform. We’re going to define a function that takes the modulus of every value in the vector, and see if it is equal to zero. If it is equal to zero, we’ll keep the value, otherwise we’ll set the value to zero.
>>> transform_f = lambda v,i,m: np.where(v % 3 == 0, v, 0)
>>> mult_of_three = tform = table.transform(transform_f, inplace=False)
>>> print mult_of_three
# Constructed from biom file
#OTU ID S0 S1 S2 S3
O0 0.0 0.0 0.0 3.0
O1 0.0 0.0 6.0 0.0
O2 0.0 9.0 0.0 0.0
O3 12.0 0.0 0.0 15.0
O4 0.0 0.0 18.0 0.0
O5 0.0 21.0 0.0 0.0
O6 24.0 0.0 0.0 27.0
O7 0.0 0.0 30.0 0.0
O8 0.0 33.0 0.0 0.0
O9 36.0 0.0 0.0 39.0
Next, we’re going to collapse the table over the phylum level taxon. To do this, we’re going to define a helper variable for the index position of the phylum (see the construction of the table above). Next, we’re going to pass this to Table.collapse, and since we want to collapse over the observations, we’ll need to specify ‘observation’ as the axis.
>>> phylum_idx = 1
>>> collapse_f = lambda id_, md: '; '.join(md['taxonomy'][:phylum_idx + 1])
>>> collapsed = mult_of_three.collapse(collapse_f, axis='observation')
>>> print collapsed
# Constructed from biom file
#OTU ID S0 S1 S2 S3
Bacteria; Firmicutes 7.2 6.6 7.2 8.4
Bacteria; Bacteroidetes 12.0 10.5 0.0 13.5
Bacteria; Proteobacteria 4.0 3.0 6.0 5.0
Finally, let’s convert the table to presence/absence data.
>>> pa = collapsed.pa()
>>> print pa
# Constructed from biom file
#OTU ID S0 S1 S2 S3
Bacteria; Firmicutes 1.0 1.0 1.0 1.0
Bacteria; Bacteroidetes 1.0 1.0 0.0 1.0
Bacteria; Proteobacteria 1.0 1.0 1.0 1.0
Converting between file formats¶
- The
convertcommand in the biom-format project can be used to convert between biom and tab-delimited table formats. This is useful for several reasons: - converting biom format tables to tab-delimited tables for easy viewing in programs such as Excel
- converting between sparse and dense biom formats
Note
The tab-delimited tables are commonly referred to as the classic format tables, while BIOM formatted tables are referred to as biom tables.
General usage examples¶
Convert a tab-delimited table to a HDF5 or JSON biom format. Note that you must specify the type of table here:
biom convert -i table.txt -o table.from_txt_json.biom --table-type="OTU table" --to-json
biom convert -i table.txt -o table.from_txt_hdf5.biom --table-type="OTU table" --to-hdf5
Convert biom format to tab-delimited table format:
biom convert -i table.biom -o table.from_biom.txt --to-tsv
Convert biom format to classic format, including the taxonomy observation metadata as the last column of the classic format table. Because the BIOM format can support an arbitrary number of observation (or sample) metadata entries, and the classic format can support only a single observation metadata entry, you must specify which of the observation metadata entries you want to include in the output table:
biom convert -i table.biom -o table.from_biom_w_taxonomy.txt --to-tsv --header-key taxonomy
Convert biom format to classic format, including the taxonomy observation metadata as the last column of the classic format table, but renaming that column as ConsensusLineage. This is useful when using legacy tools that require a specific name for the observation metadata column.:
biom convert -i table.biom -o table.from_biom_w_consensuslineage.txt --to-tsv --header-key taxonomy --output-metadata-id "ConsensusLineage"
Special case usage examples¶
Round-tripping between biom and tsv¶
In specific cases, see this comment, it is still useful to convert our biom table to tsv so we can open in Excel, make some changes to the file and then convert back to biom. For this cases you should follow this steps:
Convert from biom to txt:
biom convert -i otu_table.biom -o otu_table.txt --to-tsv --header-key taxonomy
Make your changes in Excel.
Convert back to biom:
biom convert -i otu_table.txt -o new_otu_table.biom --to-hdf5 --table-type="OTU table" --process-obs-metadata taxonomy
Converting QIIME 1.4.0 and earlier OTU tables to BIOM format¶
If you are converting a QIIME 1.4.0 or earlier OTU table to BIOM format, there are a few steps to go through. First, for convenience, you might want to rename the ConsensusLineage column taxonomy. You can do this with the following command:
sed 's/Consensus Lineage/ConsensusLineage/' < otu_table.txt | sed 's/ConsensusLineage/taxonomy/' > otu_table.taxonomy.txt
Then, you’ll want to perform the conversion including a step to convert the taxonomy string from the classic OTU table to a taxonomy list, as it’s represented in QIIME 1.4.0-dev and later:
biom convert -i otu_table.taxonomy.txt -o otu_table.from_txt.biom --table-type="OTU table" --process-obs-metadata taxonomy --to-hdf5
Adding sample and observation metadata to biom files¶
Frequently you’ll have an existing BIOM file and want to add sample and/or observation metadata to it. For samples, metadata is frequently environmental or technical details about your samples: the subject that a sample was collected from, the pH of the sample, the PCR primers used to amplify DNA from the samples, etc. For observations, metadata is frequently a categorization of the observation: the taxonomy of an OTU, or the EC hierarchy of a gene. You can use the biom add-metadata command to add this information to an existing BIOM file.
To get help with add-metadata you can call:
biom add-metadata -h
This command takes a BIOM file, and corresponding sample and/or observation mapping files. The following examples are used in the commands below. You can find these files in the biom-format/examples directory.
Your BIOM file might look like the following:
{
"id":null,
"format": "1.0.0",
"format_url": "http://biom-format.org",
"type": "OTU table",
"generated_by": "some software package",
"date": "2011-12-19T19:00:00",
"rows":[
{"id":"GG_OTU_1", "metadata":null},
{"id":"GG_OTU_2", "metadata":null},
{"id":"GG_OTU_3", "metadata":null},
{"id":"GG_OTU_4", "metadata":null},
{"id":"GG_OTU_5", "metadata":null}
],
"columns": [
{"id":"Sample1", "metadata":null},
{"id":"Sample2", "metadata":null},
{"id":"Sample3", "metadata":null},
{"id":"Sample4", "metadata":null},
{"id":"Sample5", "metadata":null},
{"id":"Sample6", "metadata":null}
],
"matrix_type": "sparse",
"matrix_element_type": "int",
"shape": [5, 6],
"data":[[0,2,1],
[1,0,5],
[1,1,1],
[1,3,2],
[1,4,3],
[1,5,1],
[2,2,1],
[2,3,4],
[2,5,2],
[3,0,2],
[3,1,1],
[3,2,1],
[3,5,1],
[4,1,1],
[4,2,1]
]
}
A sample metadata mapping file could then look like the following. Notice that there is an extra sample in here with respect to the above BIOM table. Any samples in the mapping file that are not in the BIOM file are ignored.
#SampleID BarcodeSequence DOB
# Some optional
# comment lines...
Sample1 AGCACGAGCCTA 20060805
Sample2 AACTCGTCGATG 20060216
Sample3 ACAGACCACTCA 20060109
Sample4 ACCAGCGACTAG 20070530
Sample5 AGCAGCACTTGT 20070101
Sample6 AGCAGCACAACT 20070716
An observation metadata mapping file might look like the following. Notice that there is an extra observation in here with respect to the above BIOM table. Any observations in the mapping file that are not in the BIOM file are ignored.
#OTUID taxonomy confidence
# Some optional
# comment lines
GG_OTU_0 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__ 0.980
GG_OTU_1 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 0.665
GG_OTU_2 Root;k__Bacteria 0.980
GG_OTU_3 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 1.000
GG_OTU_4 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 0.842
GG_OTU_5 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 1.000
Adding metadata¶
To add sample metadata to a BIOM file, you can run the following:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_smd.biom --sample-metadata-fp sam_md.txt
To add observation metadata to a BIOM file, you can run the following:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_omd.biom --observation-metadata-fp obs_md.txt
You can also combine these in a single command to add both observation and sample metadata:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_md.biom --observation-metadata-fp obs_md.txt --sample-metadata-fp sam_md.txt
In the last case, the resulting BIOM file will look like the following:
{
"columns": [
{
"id": "Sample1",
"metadata": {
"BarcodeSequence": "AGCACGAGCCTA",
"DOB": "20060805"
}
},
{
"id": "Sample2",
"metadata": {
"BarcodeSequence": "AACTCGTCGATG",
"DOB": "20060216"
}
},
{
"id": "Sample3",
"metadata": {
"BarcodeSequence": "ACAGACCACTCA",
"DOB": "20060109"
}
},
{
"id": "Sample4",
"metadata": {
"BarcodeSequence": "ACCAGCGACTAG",
"DOB": "20070530"
}
},
{
"id": "Sample5",
"metadata": {
"BarcodeSequence": "AGCAGCACTTGT",
"DOB": "20070101"
}
},
{
"id": "Sample6",
"metadata": {
"BarcodeSequence": "AGCAGCACAACT",
"DOB": "20070716"
}
}
],
"data": [
[0, 2, 1.0],
[1, 0, 5.0],
[1, 1, 1.0],
[1, 3, 2.0],
[1, 4, 3.0],
[1, 5, 1.0],
[2, 2, 1.0],
[2, 3, 4.0],
[2, 5, 2.0],
[3, 0, 2.0],
[3, 1, 1.0],
[3, 2, 1.0],
[3, 5, 1.0],
[4, 1, 1.0],
[4, 2, 1.0]
],
"date": "2012-12-11T07:36:15.467843",
"format": "Biological Observation Matrix 1.0.0",
"format_url": "http://biom-format.org",
"generated_by": "some software package",
"id": null,
"matrix_element_type": "float",
"matrix_type": "sparse",
"rows": [
{
"id": "GG_OTU_1",
"metadata": {
"confidence": "0.665",
"taxonomy": "Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae"
}
},
{
"id": "GG_OTU_2",
"metadata": {
"confidence": "0.980",
"taxonomy": "Root;k__Bacteria"
}
},
{
"id": "GG_OTU_3",
"metadata": {
"confidence": "1.000",
"taxonomy": "Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae"
}
},
{
"id": "GG_OTU_4",
"metadata": {
"confidence": "0.842",
"taxonomy": "Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae"
}
},
{
"id": "GG_OTU_5",
"metadata": {
"confidence": "1.000",
"taxonomy": "Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae"
}
}
],
"shape": [5, 6],
"type": "OTU table"
}
Processing metadata while adding¶
There are some additional parameters you can pass to this command for more complex processing.
You can tell the command to process certain metadata column values as integers (--int-fields), floating point (i.e., decimal or real) numbers (--float-fields), or as hierarchical semicolon-delimited data (--sc-separated).
biom add-metadata -i min_sparse_otu_table.biom -o table.w_md.biom --observation-metadata-fp obs_md.txt --sample-metadata-fp sam_md.txt --int-fields DOB --sc-separated taxonomy --float-fields confidence
Here your resulting BIOM file will look like the following, where DOB values are now integers (compare to the above: they’re not quoted now), confidence values are now floating point numbers (again, not quoted now), and taxonomy values are now lists where each entry is a taxonomy level, opposed to above where they appear as a single semi-colon-separated string.
{
"columns": [
{
"id": "Sample1",
"metadata": {
"BarcodeSequence": "AGCACGAGCCTA",
"DOB": 20060805
}
},
{
"id": "Sample2",
"metadata": {
"BarcodeSequence": "AACTCGTCGATG",
"DOB": 20060216
}
},
{
"id": "Sample3",
"metadata": {
"BarcodeSequence": "ACAGACCACTCA",
"DOB": 20060109
}
},
{
"id": "Sample4",
"metadata": {
"BarcodeSequence": "ACCAGCGACTAG",
"DOB": 20070530
}
},
{
"id": "Sample5",
"metadata": {
"BarcodeSequence": "AGCAGCACTTGT",
"DOB": 20070101
}
},
{
"id": "Sample6",
"metadata": {
"BarcodeSequence": "AGCAGCACAACT",
"DOB": 20070716
}
}
],
"data": [
[0, 2, 1.0],
[1, 0, 5.0],
[1, 1, 1.0],
[1, 3, 2.0],
[1, 4, 3.0],
[1, 5, 1.0],
[2, 2, 1.0],
[2, 3, 4.0],
[2, 5, 2.0],
[3, 0, 2.0],
[3, 1, 1.0],
[3, 2, 1.0],
[3, 5, 1.0],
[4, 1, 1.0],
[4, 2, 1.0]
],
"date": "2012-12-11T07:30:29.870689",
"format": "Biological Observation Matrix 1.0.0",
"format_url": "http://biom-format.org",
"generated_by": "some software package",
"id": null,
"matrix_element_type": "float",
"matrix_type": "sparse",
"rows": [
{
"id": "GG_OTU_1",
"metadata": {
"confidence": 0.665,
"taxonomy": ["Root", "k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Clostridiales", "f__Lachnospiraceae"]
}
},
{
"id": "GG_OTU_2",
"metadata": {
"confidence": 0.98,
"taxonomy": ["Root", "k__Bacteria"]
}
},
{
"id": "GG_OTU_3",
"metadata": {
"confidence": 1.0,
"taxonomy": ["Root", "k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Clostridiales", "f__Lachnospiraceae"]
}
},
{
"id": "GG_OTU_4",
"metadata": {
"confidence": 0.842,
"taxonomy": ["Root", "k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Clostridiales", "f__Lachnospiraceae"]
}
},
{
"id": "GG_OTU_5",
"metadata": {
"confidence": 1.0,
"taxonomy": ["Root", "k__Bacteria", "p__Firmicutes", "c__Clostridia", "o__Clostridiales", "f__Lachnospiraceae"]
}
}
],
"shape": [5, 6],
"type": "OTU table"
}
If you have multiple fields that you’d like processed in one of these ways, you can pass a comma-separated list of field names (e.g., --float-fields confidence,pH).
Renaming (or naming) metadata columns while adding¶
You can also override the names of the metadata fields provided in the mapping files with the --observation-header and --sample-header parameters. This is useful if you want to rename metadata columns, or if metadata column headers aren’t present in your metadata mapping file. If you pass either of these parameters, you must name all columns in order. If there are more columns in the metadata mapping file then there are headers, extra columns will be ignored (so this is also a useful way to select only the first n columns from your mapping file). For example, if you want to rename the DOB column in the sample metadata mapping you could do the following:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_smd.biom --sample-metadata-fp sam_md.txt --sample-header SampleID,BarcodeSequence,DateOfBirth
If you have a mapping file without headers such as the following:
GG_OTU_0 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__ 0.980
GG_OTU_1 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 0.665
GG_OTU_2 Root;k__Bacteria 0.980
GG_OTU_3 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 1.000
GG_OTU_4 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 0.842
GG_OTU_5 Root;k__Bacteria;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae 1.000
you could name these while adding them as follows:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_omd.biom --observation-metadata-fp obs_md.txt --observation-header OTUID,taxonomy,confidence
As a variation on the last command, if you only want to include the taxonomy column and exclude the confidence column, you could run:
biom add-metadata -i min_sparse_otu_table.biom -o table.w_omd.biom --observation-metadata-fp obs_md.txt --observation-header OTUID,taxonomy
Summarizing BIOM tables¶
If you have an existing BIOM file and want to compile a summary of the information in that table, you can use the biom summarize-table command.
To get help with biom summarize-table you can call:
biom summarize-table -h
This command takes a BIOM file or gzipped BIOM file as input, and will print a summary of the count information on a per-sample basis to the new file specified by the -o parameter. The example file used in the commands below can be found in the biom-format/examples directory.
Summarizing sample data¶
To summarize the per-sample data in a BIOM file, you can run:
biom summarize-table -i rich_sparse_otu_table.biom -o rich_sparse_otu_table_summary.txt
The following information will be written to rich_sparse_otu_table_summary.txt:
Num samples: 6
Num observations: 5
Total count: 27
Table density (fraction of non-zero values): 0.500
Counts/sample summary:
Min: 3.0
Max: 7.0
Median: 4.000
Mean: 4.500
Std. dev.: 1.500
Sample Metadata Categories: LinkerPrimerSequence; BarcodeSequence; Description; BODY_SITE
Observation Metadata Categories: taxonomy
Counts/sample detail:
Sample5: 3.0
Sample2: 3.0
Sample6: 4.0
Sample3: 4.0
Sample4: 6.0
Sample1: 7.0
As you can see, general summary information about the table is provided, including the number of samples, the number of observations, the total count (i.e., the sum of all values in the table), and so on, followed by the per-sample counts.
Summarizing sample data qualitatively¶
To summarize the per-sample data in a BIOM file qualitatively, where the number of unique observations per sample (rather than the total count of observations per sample) are provided, you can run:
biom summarize-table -i rich_sparse_otu_table.biom --qualitative -o rich_sparse_otu_table_qual_summary.txt
The following information will be written to rich_sparse_otu_table_qual_summary.txt:
Num samples: 6
Num observations: 5
Observations/sample summary:
Min: 1
Max: 4
Median: 2.500
Mean: 2.500
Std. dev.: 0.957
Sample Metadata Categories: LinkerPrimerSequence; BarcodeSequence; Description; BODY_SITE
Observation Metadata Categories: taxonomy
Observations/sample detail:
Sample5: 1
Sample4: 2
Sample1: 2
Sample6: 3
Sample2: 3
The BIOM Format License¶
The BIOM Format project is licensed under the terms of the Modified BSD
License (also known as New or Revised BSD), as follows:
Copyright (c) 2011-2014, The BIOM Format Development Team <gregcaporaso@gmail.com>
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the BIOM Format Development Team nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE BIOM FORMAT DEVELOPMENT TEAM BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The following banner should be used in any source code file to indicate the
copyright and license terms:
#-----------------------------------------------------------------------------
# Copyright (c) 2011-2014, The BIOM Format Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
BIOM version¶
The latest official version of the biom-format project is 2.1.4 and of the BIOM file format is 2.0. Details on the file format can be found here.
Installing the biom-format project¶
To install the biom-format project, you can download the latest version here, or work with the development version. Generally we recommend working with the release version as it will be more stable, but if you want access to the latest features (and can tolerate some instability) you should work with the development version.
The biom-format project has the following dependencies:
The easiest way to install the latest version of the biom-format project and its required dependencies is via pip:
pip install numpy
pip install biom-format
That’s it!
If you decided not to install biom-format using pip, it is also possible to manually install the latest release. We’ll illustrate the install process in the $HOME/code directory. You can either work in this directory on your system (creating it, if necessary, by running mkdir $HOME/code) or replace all occurrences of $HOME/code in the following instructions with your working directory. Please note that numpy must be in your installed prior to installing biom-format. Change to this directory to start the install process:
cd $HOME/code
Download the latest release, which can be found here. After downloading, unpack and install (note: x.y.z refers to the downloaded version):
tar xzf biom-format-x.y.z.tar.gz
cd $HOME/code/biom-format-x.y.z
Alternatively, to install the development version, pull it from GitHub, and change to the resulting directory:
git clone git://github.com/biocore/biom-format.git
cd $HOME/code/biom-format
To install (either the development or release version), follow these steps:
sudo python setup.py install
If you do not have sudo access on your system (or don’t want to install the biom-format project in the default location) you’ll need to install the library code and scripts in specified directories, and then tell your system where to look for those files. You can do this as follows:
echo "export PATH=$HOME/bin/:$PATH" >> $HOME/.bashrc
echo "export PYTHONPATH=$HOME/lib/:$PYTHONPATH" >> $HOME/.bashrc
mkdir -p $HOME/bin $HOME/lib/
source $HOME/.bashrc
python setup.py install --install-scripts=$HOME/bin/ --install-purelib=$HOME/lib/ --install-lib=$HOME/lib/
You should then have access to the biom-format project. You can test this by running the following command:
python -c "from biom import __version__; print __version__"
You should see the current version of the biom-format project.
Next you can run:
which biom
You should get a file path ending with biom printed to your screen if it is installed correctly. Finally, to see a list of all biom commands, run:
biom
Enabling tab completion of biom commands¶
The biom command referenced in the previous section is a driver for commands in biom-format, powered by the pyqi project. You can enable tab completion of biom command names and command options (meaning that when you begin typing the name of a command or option you can auto-complete it by hitting the tab key) by following a few simple steps from the pyqi documentation. While this step is optional, tab completion is very convenient so it’s worth enabling.
To enable tab completion, follow the steps outlined under Configuring bash completion in the pyqi install documentation, substituting biom for my-project and my_project in all commands. After completing those steps and closing and re-opening your terminal, auto-completion should be enabled.
BIOM format in R¶
There is also a BIOM format package for R, called biom. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object, as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly “R flavor” that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods.
To install the latest stable release of the biom package enter the following command from within an R session:
install.packages("biom")
To install the latest development version of the biom package, enter the following lines in an R session:
install.packages("devtools") # if not already installed
library("devtools")
install_github("biom", "joey711")
Please post any support or feature requests and bugs to the biom issue tracker.
See the biom project on GitHub for further details, or if you would like to contribute.
Note that the licenses between the biom R package (GPL-2) and the other biom-format software (Modified BSD) are different.
Citing the BIOM project¶
You can cite the BIOM format as follows (link):
Development team¶
The biom-format project was conceived of and developed by the QIIME, MG-RAST, and VAMPS development groups to support interoperability of our software packages. If you have questions about the biom-format project you can contact gregcaporaso@gmail.com.