Difference between revisions of "Khmer"

Latest revision as of 17:01, 14 December 2022

Description

Khmer - python scripts for k-mer counting, filtering and graph traversal.

Available scripts: abundance-dist.py, count-median.py, do-partition.sh, filter-abund.py, find-knots.py, load-into-counting.py, merge-partitions.py, normalize-by-median.py, partition-graph.py, annotate-partitions.py, count-overlap.py, extract-partitions.py, filter-stoptags.py, load-graph.py, make-initial-stoptags.py, normalize-by-kadian.py, normalize-by-min.py

Use "import khmer" in your script or in an interactive python session.

Environment Modules

Run module spider khmer to find out what environment modules are available for this application.

System Variables

HPC_KHMER_DIR - installation directory
HPC_KHMER_BIN
HPC_KHMER_LIB

Citation

If you use the khmer software, you must cite:

Crusoe et al., The khmer software package: enabling efficient sequence analysis. 2014. doi: 10.6084/m9.figshare.979190

If you use any of khmer's published scientific methods, you should *also* cite the relevant paper(s), as directed below.

Expand to view citation instructions.

Graph partitioning and/or compressible graph representation

The load-graph.py, partition-graph.py, find-knots.py, load-graph.py, and partition-graph.py scripts are part of the compressible graph representation and partitioning algorithms described in:

Pell J, Hintze A, Canino-Koning R, Howe A, Tiedje JM, Brown CT

Proc Natl Acad Sci U S A. 2012 Aug 14;109(33):13272-7

doi: 10.1073/pnas.1121464109

PMID: 22847406

Digital normalization

The normalize-by-median.py and count-median.py scripts are part of the digital normalization algorithm, described in:

A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data

Brown CT, Howe AC, Zhang Q, Pyrkosz AB, Brom TH

arXiv:1203.4802 [q-bio.GN]

http://arxiv.org/abs/1203.4802

K-mer counting

The abundance-dist.py, filter-abund.py, and load-into-counting.py scripts implement the probabilistic k-mer counting described in:

These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure

Zhang Q, Pell J, Canino-Koning R, Howe AC, Brown CT.

arXiv:1309.2975 [q-bio.GN]

http://arxiv.org/abs/1309.2975

@@ Line 1: / Line 1: @@
 __NOTOC__
 __NOEDITSECTION__
-[[Category:Software]][[Category:Bioinformatics]][[Category:NGS]]
+[[Category:Software]][[Category:Biology]][[Category:NGS]]
 {|<!--Main settings - REQUIRED-->
 |{{#vardefine:app|khmer}}
-|{{#vardefine:url|https://github.com/ctb/khmer}}
+|{{#vardefine:url|https://github.com/ged-lab/khmer}}
 |{{#vardefine:exe|}} <!--Present manual instructions for running the software -->
 |{{#vardefine:conf|}} <!--Enable config wiki page link - {{#vardefine:conf|1}} = ON/conf|}} = OFF-->
@@ Line 11: / Line 11: @@
 |{{#vardefine:testing|}} <!--Enable performance testing/profiling section -->
 |{{#vardefine:faq|}} <!--Enable FAQ section -->
-|{{#vardefine:citation|}} <!--Enable Reference/Citation section -->
+|{{#vardefine:citation|1}} <!--Enable Reference/Citation section -->
 |}
 <!-- ########  Template Body ######## -->
@@ Line 17: / Line 17: @@
 {{#if: {{#var: url}}|
 {{App_Description|app={{#var:app}}|url={{#var:url}}|name={{#var:app}}}}|}}
- Khmer - python scripts for k-mer counting, filtering and graph traversal.
-There's a khmer mailing list at librelist.com that you can use to get help with khmer. To sign up, email 'khmer@librelist.com'  to subscribe; then send your question/comment there.
+Khmer - python scripts for k-mer counting, filtering and graph traversal.
-'''IMPORTANT NOTE:'''
-khmer is *pre-publication* and *research* software, so please keep in mind that (a) the code may have undiscovered bugs in it, (b) you should cite us, and (c) you should get in touch if you need to cite us, as we are writing up the project.
 Available scripts: abundance-dist.py, count-median.py, do-partition.sh, filter-abund.py, find-knots.py, load-into-counting.py, merge-partitions.py, normalize-by-median.py, partition-graph.py, annotate-partitions.py, count-overlap.py, extract-partitions.py, filter-stoptags.py, load-graph.py, make-initial-stoptags.py, normalize-by-kadian.py, normalize-by-min.py
@@ Line 29: / Line 24: @@
 Use "import khmer" in your script or in an interactive python session.
 <!--Modules-->
-==Required Modules==
+==Environment Modules==
-[[Modules|modules documentation]]
+Run <code>module spider {{#var:app}}</code> to find out what environment modules are available for this application.
-===Serial===
-*{{#var:app}}
 ==System Variables==
-* HPC_{{#uppercase:{{#var:app}}}}_DIR - installation directory
+* HPC_{{uc:{{#var:app}}}}_DIR - installation directory
-* HPC_KHMER_BIN - directory where the scripts are located
+* HPC_KHMER_BIN
-* HPC_KHMER_DOC - khmer documents are in this directory
+* HPC_KHMER_LIB
-* HPC_KHMER_DATA - sample datasets are in this directory
 {{#if: {{#var: exe}}|==How To Run==
 WRITE INSTRUCTIONS ON RUNNING THE ACTUAL BINARY|}}
@@ Line 51: / Line 43: @@
 *'''Q:''' **'''A:'''|}}
 {{#if: {{#var: citation}}|==Citation==
-If you publish research that uses {{{app}}} you have to cite it as follows:
+If you use the khmer software, you must cite:
-WRITE CITATION HERE
+: Crusoe et al., The khmer software package: enabling efficient sequence analysis. 2014. doi: 10.6084/m9.figshare.979190
+If you use any of khmer's published scientific methods, you should *also* cite the relevant paper(s), as directed below.
+<div class="mw-collapsible mw-collapsed" style="width:70%; padding: 5px; border: 1px solid gray;">
+''Expand to view citation instructions.''
+<div class="mw-collapsible-content" style="padding: 5px;">
+* Graph partitioning and/or compressible graph representation
+: The load-graph.py, partition-graph.py, find-knots.py, load-graph.py, and partition-graph.py scripts are part of the compressible graph representation and partitioning algorithms described in:
+:: Pell J, Hintze A, Canino-Koning R, Howe A, Tiedje JM, Brown CT
+:: Proc Natl Acad Sci U S A. 2012 Aug 14;109(33):13272-7
+:: doi: 10.1073/pnas.1121464109
+:: PMID: 22847406
+* Digital normalization
+: The normalize-by-median.py and count-median.py scripts are part of the digital normalization algorithm, described in:
+:: A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data
+:: Brown CT, Howe AC, Zhang Q, Pyrkosz AB, Brom TH
+:: arXiv:1203.4802 [q-bio.GN]
+:: http://arxiv.org/abs/1203.4802
+* K-mer counting
+: The abundance-dist.py, filter-abund.py, and load-into-counting.py scripts implement the probabilistic k-mer counting described in:
+:: These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure
+:: Zhang Q, Pell J, Canino-Koning R, Howe AC, Brown CT.
+:: arXiv:1309.2975 [q-bio.GN]
+:: http://arxiv.org/abs/1309.2975
+</div>
+</div>
 |}}