<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/34618?offset=320 https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping Tue, 08 May 2018 04:15:25 -0500 https://bioinformaticsonline.com/bookmarks/view/36508/mitobim-mitochondrial-baiting-and-iterative-mapping <![CDATA[MITObim - mitochondrial baiting and iterative mapping]]> This document contains instructions on how to use the MITObim pipeline described in Hahn et al. 2013. The full article can be found here. Kindly cite the article if you are using MITObim in your work. The pipeline was originally developed for Illumina data, but thanks to the versatility of the MIRA assembler, MITObim supports in principle also data from the Iontorrent, 454 and PacBio sequencing platforms.

Below you can find a few basic tutorials for how to run MITObim and I encorage you to give them a try with the testdata that comes with this Repo, just to make sure everything is running smoothly on your system. It'll only take a few minutes and will potentially safe you a lot of time down the line.

I provide further examples here as Jupyter notebooks. Get in touch if you feel like sharing your particular MITObim solution and I'd be happy to put it up here, too!

Address of the bookmark: https://github.com/chrishah/MITObim

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/37800/heatmapper-web-enabled-heat-mapping-for-all Mon, 01 Oct 2018 08:34:41 -0500 https://bioinformaticsonline.com/bookmarks/view/37800/heatmapper-web-enabled-heat-mapping-for-all <![CDATA[Heatmapper: web-enabled heat mapping for all]]> Heatmapper is a freely available web server that allows users to interactively visualize their data in the form of heat maps through an easy-to-use graphical interface. Heatmapper is a versatile tool that allows users to easily create a wide variety of heat maps for many different data types and applications. Heatmapper allows users to generate, cluster and visualize:

1) expression-based heat maps from transcriptomic, proteomic and metabolomic experiments; 2) pairwise distance maps;

3) correlation maps;

4) image overlay heat maps;

5) latitude and longitude heat maps and

6) geopolitical (choropleth) heat maps.

Heatmapper offers a number of simple and intuitive customization options for easy adjustments to each heat map’s appearance and plotting parameters. Heatmapper also allows users to interactively explore their numeric data values by hovering their cursor over each heat map, or by using a searchable/sortable data table view.

Ref https://www.ncbi.nlm.nih.gov/pubmed/27190236

Address of the bookmark: http://www2.heatmapper.ca/

]]> Jit https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel Fri, 30 May 2014 13:24:11 -0500 https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel <![CDATA[How to sequence the human genome - Mark J. Kiel]]> View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence. Lesson by Mark J. Kiel, animation by Marc Christoforidis.]]> https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping Fri, 20 May 2016 18:53:49 -0500 https://bioinformaticsonline.com/bookmarks/view/27430/mosaik-a-hash-based-algorithm-for-accurate-next-generation-sequencing-short-read-mapping <![CDATA[MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping]]> MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery.

Address of the bookmark: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090581

]]> Neel https://bioinformaticsonline.com/bookmarks/view/32018/tmap-torrent-mapping-alignment-program-general-notes Sun, 02 Apr 2017 15:53:47 -0500 https://bioinformaticsonline.com/bookmarks/view/32018/tmap-torrent-mapping-alignment-program-general-notes <![CDATA[TMAP - torrent mapping alignment program General Notes]]> TMAP - torrent mapping alignment program General Notes

TMAP is a fast and accurate alignment software for short and long nucleotide sequences produced by next-generation sequencing technologies.

  • The latest TMAP is unsupported. To use a supported version, please see the TMAP version associated with a Torrent Suite release below.

  • Get the latest source code:

    git clone git://github.com/iontorrent/TMAP.git
 cd TMAP
 git submodule init
 git submodule update

https://github.com/iontorrent/TS/tree/master/Analysis/TMAP

Address of the bookmark: https://github.com/iontorrent/TS/tree/master/Analysis/TMAP

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/39453/fuma-gwas-functional-mapping-and-annotation-of-genome-wide-association-studies Sat, 01 Jun 2019 03:11:16 -0500 https://bioinformaticsonline.com/bookmarks/view/39453/fuma-gwas-functional-mapping-and-annotation-of-genome-wide-association-studies <![CDATA[FUMA GWAS: Functional Mapping and Annotation of Genome-Wide Association Studies]]> FUMA is a platform that can be used to annotate, prioritize, visualize and interpret GWAS results. 
The SNP2GENE function takes GWAS summary statistics as an input, and provides extensive functional annotation for all SNPs in genomic areas identified by lead SNPs. 
The GENE2FUNC function takes a list of gene IDs (as identified by SNP2GENE or as provided manually) and annotates genes in biological context 

Address of the bookmark: https://fuma.ctglab.nl/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/40871/nanopore-adaptor Mon, 03 Feb 2020 00:10:29 -0600 https://bioinformaticsonline.com/bookmarks/view/40871/nanopore-adaptor <![CDATA[Nanopore adaptor !]]> Porechop is a tool for finding and removing adapters from Oxford Nanopore reads. Adapters on the ends of reads are trimmed off, and when a read has an adapter in its middle, it is treated as chimeric and chopped into separate reads. Porechop performs thorough alignments to effectively find adapters, even at low sequence identity.

Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

The known Nanopore adapters that Porechop looks for are defined

https://github.com/rrwick/Porechop/blob/master/porechop/adapters.py

They are:

  • Ligation kit adapters
  • Rapid kit adapters
  • PCR kit adapters
  • Barcodes
  • Native barcoding
  • Rapid barcoding

Address of the bookmark: https://github.com/rrwick/Porechop/blob/master/porechop/adapters.py

]]> Jit https://bioinformaticsonline.com/bookmarks/view/36403/ngmlr-long-read-mapper-designed-to-align-pacbio-or-oxford-nanopore Wed, 25 Apr 2018 07:30:54 -0500 https://bioinformaticsonline.com/bookmarks/view/36403/ngmlr-long-read-mapper-designed-to-align-pacbio-or-oxford-nanopore <![CDATA[NGMLR: long-read mapper designed to align PacBio or Oxford Nanopore]]> CoNvex Gap-cost alignMents for Long Reads (ngmlr) is a long-read mapper designed to sensitively align PacBilo or Oxford Nanopore to (large) reference genomes. It was designed to quickly and correctly align the reads, including those spanning (complex) structural variations. Ngmlr uses an SV aware k-mer search to find approximate mapping locations for a read and then a banded Smith-Waterman alignment algorithm to compute the final alignment. Ngmlr uses a convex gap cost model that penalizes gap extensions for longer gaps less than for shorter ones to compute precise alignments. The gap model allows ngmlr to account for both the sequencing error and real genomic variations at the same time and makes it especially effective at more precisely identifying the position of breakpoints stemming from structural variations. The k-mer search helps to detect and split reads that cannot be aligned linearly, enabling ngmlr to reliably align reads to a wide range of different structural variations including nested SVs (e.g. inversions flanked by deletions).

Address of the bookmark: https://github.com/philres/ngmlr

]]> Jit https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 Mon, 10 Apr 2017 04:45:40 -0500 https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 <![CDATA[WGS Celera Assembler version 8.3rc2]]> These are release notes for Celera Assembler version 8.3rc2, which was released on May 24, 2015.

This distribution package provides a stable, tested, documented version of the software.  The distribution is usable on most Unix-like platforms, and some platforms have pre-compiled binary distributions ready for installation.

The source code package includes full source code (revision 4627), Makefiles, and scripts.  A subset of the kmer package (http://kmer.sourceforge.net/, version r1994), used by some modules of Celera Assembler, is included.  This distribution includes [http://samtools.sourceforge.net/ SAMtools], [http://www.cbcb.umd.edu/software/jellyfish/ Jellyfish 2.0], [https://github.com/pbjd/pbutgcns PBUTGCNS], [https://github.com/PacificBiosciences/pbdagcon PBDAGCON], [https://github.com/PacificBiosciences/BLASR BLASR], and parts of the [https://github.com/PacificBiosciences/FALCON/tree/v0.1.3 Falcon assembler].

Full documentation can be found online at http://wgs-assembler.sourceforge.net/.

Interesting scripts within it

urbe@urbo214b[bin] ls                                                        []
-rwxrwxr-x 1 urbe urbe  11K Apr 10 11:41 addCNSToStore
-rwxrwxr-x 1 urbe urbe 575K Apr 10 11:41 addReadsToUnitigs
-rwxrwxr-x 1 urbe urbe 128K Apr 10 11:41 analyzeBest
-rwxrwxr-x 1 urbe urbe 257K Apr 10 11:41 analyzePosMap
-rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 analyzeScaffolds
-rwxrwxr-x 1 urbe urbe 224K Apr 10 11:41 asmOutputFasta
-rwxrwxr-x 1 urbe urbe 448K Apr 10 11:41 asmOutputStatistics
-rwxrwxr-x 1 urbe urbe 2,4K Apr 10 11:41 asmToAGP.pl
-rwxrwxr-x 1 urbe urbe 7,6M Apr 10 11:41 blasr
-rwxrwxr-x 1 urbe urbe 1,6M Apr 10 11:41 bogart
-rwxrwxr-x 1 urbe urbe 183K Apr 10 11:41 bogus
-rwxrwxr-x 1 urbe urbe 272K Apr 10 11:41 bogusness
-rwxrwxr-x 1 urbe urbe 247K Apr 10 11:41 buildPosMap
-rwxrwxr-x 1 urbe urbe 213K Apr 10 11:41 buildRefContigs
-rwxrwxr-x 1 urbe urbe 990K Apr 10 11:41 buildUnitigs
-rwxrwxr-x 1 urbe urbe  18K Apr 10 11:41 ca2ace.pl
-rwxrwxr-x 1 urbe urbe  12K Apr 10 11:41 caqc_help.ini
-rwxrwxr-x 1 urbe urbe  61K Apr 10 11:41 caqc.pl
-rwxrwxr-x 1 urbe urbe  23K Apr 10 11:41 cat-corrects
-rwxrwxr-x 1 urbe urbe  24K Apr 10 11:41 cat-erates
-rwxrwxr-x 1 urbe urbe 1,9M Apr 10 11:41 cgw
-rwxrwxr-x 1 urbe urbe 1,4M Apr 10 11:41 cgwDump
-rwxrwxr-x 1 urbe urbe 204K Apr 10 11:41 chimChe
-rwxrwxr-x 1 urbe urbe 201K Apr 10 11:40 chimera
-rwxrwxr-x 1 urbe urbe 220K Apr 10 11:41 classifyMates
-rwxrwxr-x 1 urbe urbe 201K Apr 10 11:41 classifyMatesApply
-rwxrwxr-x 1 urbe urbe 215K Apr 10 11:41 classifyMatesPairwise
-rwxrwxr-x 1 urbe urbe 366K Apr 10 11:41 computeCoverageStat
-rwxrwxr-x 1 urbe urbe 9,8K Apr 10 11:41 convert-fasta-to-v2.pl
-rwxrwxr-x 1 urbe urbe  48K Apr 10 11:41 convertOverlap
-rwxrwxr-x 1 urbe urbe 119K Apr 10 11:41 convertSamToCA
-rwxrwxr-x 1 urbe urbe  20K Apr 10 11:41 convertToPBCNS
-rwxrwxr-x 1 urbe urbe 197K Apr 10 11:41 correct-frags
-rwxrwxr-x 1 urbe urbe 259K Apr 10 11:41 correct-olaps
-rwxrwxr-x 1 urbe urbe 520K Apr 10 11:41 correctPacBio
-rwxrwxr-x 1 urbe urbe 540K Apr 10 11:41 ctgcns
-rwxrwxr-x 1 urbe urbe 162K Apr 10 11:40 deduplicate
-rwxrwxr-x 1 urbe urbe  37K Apr 10 11:41 demotePosMap
-rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 dumpCloneMiddles
-rwxrwxr-x 1 urbe urbe 124K Apr 10 11:41 dumpPBRLayoutStore
-rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 dumpSingletons
-rwxrwxr-x 1 urbe urbe 171K Apr 10 11:41 erate-estimate
-rwxrwxr-x 1 urbe urbe 221K Apr 10 11:40 estimate-mer-threshold
-rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 extendClearRanges
-rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 extendClearRangesPartition
-rwxrwxr-x 1 urbe urbe 205K Apr 10 11:40 extractmessages
-rwxrwxr-x 1 urbe urbe 7,2M Apr 10 11:41 falcon_sense
-rwxrwxr-x 1 urbe urbe 9,8K Apr 10 11:41 fastaToCA
-rwxrwxr-x 1 urbe urbe 124K Apr 10 11:40 fastqAnalyze
-rwxrwxr-x 1 urbe urbe 137K Apr 10 11:40 fastqSample
-rwxrwxr-x 1 urbe urbe  62K Apr 10 11:40 fastqSimulate
-rwxrwxr-x 1 urbe urbe 121K Apr 10 11:40 fastqSimulate-sort
-rwxrwxr-x 1 urbe urbe 246K Apr 10 11:40 fastqToCA
-rwxrwxr-x 1 urbe urbe 140K Apr 10 11:41 filterOverlap
-rwxrwxr-x 1 urbe urbe 341K Apr 10 11:40 finalTrim
-rwxrwxr-x 1 urbe urbe 228K Apr 10 11:41 fixUnitigs
-rwxrwxr-x 1 urbe urbe 147K Apr 10 11:40 fragmentDepth
-rwxrwxr-x 1 urbe urbe  29K Apr 10 11:41 fragsInVars
-rwxrwxr-x 1 urbe urbe 545K Apr 10 11:41 frgs2clones
-rwxrwxr-x 1 urbe urbe 398K Apr 10 11:40 gatekeeper
-rwxrwxr-x 1 urbe urbe 139K Apr 10 11:40 gatekeeperbench
-rwxrwxr-x 1 urbe urbe 167K Apr 10 11:40 gkpStoreCreate
-rwxrwxr-x 1 urbe urbe 147K Apr 10 11:40 gkpStoreDumpFASTQ
-rwxrwxr-x 1 urbe urbe 184K Apr 10 11:41 greedyFragmentTiling
-rwxrwxr-x 1 urbe urbe 1,6K Apr 10 11:41 greedy_layout_to_IUM
-rwxrwxr-x 1 urbe urbe 142K Apr 10 11:40 initialTrim
-rwxrwxr-x 1 urbe urbe 967K Apr 10 11:41 jellyfish
-rwxrwxr-x 1 urbe urbe 219K Apr 10 11:41 markRepeatUnique
-rwxrwxr-x 1 urbe urbe 273K Apr 10 11:40 markUniqueUnique
-rwxrwxr-x 1 urbe urbe 114K Apr 10 11:40 mercy
-rwxrwxr-x 1 urbe urbe 3,8K Apr 10 11:41 mergeqc.pl
-rwxrwxr-x 1 urbe urbe 422K Apr 10 11:40 merTrim
-rwxrwxr-x 1 urbe urbe 125K Apr 10 11:40 merTrimApply
-rwxrwxr-x 1 urbe urbe 376K Apr 10 11:40 meryl
-rwxrwxr-x 1 urbe urbe 176K Apr 10 11:41 metagenomics_ovl_analyses
-rwxrwxr-x 1 urbe urbe 297K Apr 10 11:41 olap-from-seeds
-rwxrwxr-x 1 urbe urbe 275K Apr 10 11:41 outputLayout
-rwxrwxr-x 1 urbe urbe 229K Apr 10 11:41 overlapInCore
-rwxrwxr-x 1 urbe urbe 144K Apr 10 11:40 overlap_partition
-rwxrwxr-x 1 urbe urbe 179K Apr 10 11:41 overlapStats
-rwxrwxr-x 1 urbe urbe 179K Apr 10 11:41 overlapStore
-rwxrwxr-x 1 urbe urbe 153K Apr 10 11:41 overlapStoreBucketizer
-rwxrwxr-x 1 urbe urbe 175K Apr 10 11:41 overlapStoreBuild
-rwxrwxr-x 1 urbe urbe  33K Apr 10 11:41 overlapStoreIndexer
-rwxrwxr-x 1 urbe urbe  48K Apr 10 11:41 overlapStoreSorter
-rwxrwxr-x 1 urbe urbe 604K Apr 10 11:40 overmerry
lrwxrwxrwx 1 urbe urbe    4 Apr 10 11:41 pacBioToCA -> PBcR
-rwxrwxr-x 1 urbe urbe 131K Apr 10 11:41 PBcR
-rwxrwxr-x 1 urbe urbe 2,9M Apr 10 11:41 pbdagcon
-rwxrwxr-x 1 urbe urbe 1,9M Apr 10 11:41 pbutgcns
-rwxrwxr-x 1 urbe urbe 201K Apr 10 11:40 remove_fragment
-rwxrwxr-x 1 urbe urbe 153K Apr 10 11:40 removeMateOverlap
-rwxrwxr-x 1 urbe urbe 2,5K Apr 10 11:41 replaceUIDwithName-fastq
-rwxrwxr-x 1 urbe urbe 1,2K Apr 10 11:41 replaceUIDwithName-posmap
-rwxrwxr-x 1 urbe urbe 1,3M Apr 10 11:41 resolveSurrogates
-rwxrwxr-x 1 urbe urbe 139K Apr 10 11:41 rewriteCache
-rwxrwxr-x 1 urbe urbe 232K Apr 10 11:41 runCA
-rwxrwxr-x 1 urbe urbe  88K Apr 10 11:41 runCA-dedupe
-rwxrwxr-x 1 urbe urbe  14K Apr 10 11:41 runCA-overlapStoreBuild
-rwxrwxr-x 1 urbe urbe 3,6K Apr 10 11:41 run_greedy.csh
-rwxrwxr-x 1 urbe urbe 297K Apr 10 11:40 sffToCA
-rwxrwxr-x 1 urbe urbe  13K Apr 10 11:40 show-corrects
-rwxrwxr-x 1 urbe urbe 557K Apr 10 11:41 splitUnitigs
-rwxrwxr-x 1 urbe urbe 1,4M Apr 10 11:41 terminator
drwxrwxr-x 2 urbe urbe 4,0K Apr 10 11:41 TIGR
-rwxrwxr-x 1 urbe urbe 526K Apr 10 11:41 tigStore
-rwxrwxr-x 1 urbe urbe  35K Apr 10 11:41 tracearchiveToCA
-rwxrwxr-x 1 urbe urbe  35K Apr 10 11:41 tracedb-to-frg.pl
-rwxrwxr-x 1 urbe urbe  44K Apr 10 11:41 trimFastqByQVWindow
-rwxrwxr-x 1 urbe urbe  18K Apr 10 11:40 uidclient
-rwxrwxr-x 1 urbe urbe 589K Apr 10 11:41 unitigger
-rwxrwxr-x 1 urbe urbe  42K Apr 10 11:40 upgrade-v8-to-v9
-rwxrwxr-x 1 urbe urbe  42K Apr 10 11:40 upgrade-v9-to-v10
-rwxrwxr-x 1 urbe urbe  854 Apr 10 11:41 utg2fasta
-rwxrwxr-x 1 urbe urbe 731K Apr 10 11:41 utgcns
-rwxrwxr-x 1 urbe urbe 561K Apr 10 11:41 utgcnsfix


Address of the bookmark: http://wgs-assembler.sourceforge.net/wiki/index.php/Main_Page

]]> Jit https://bioinformaticsonline.com/pages/view/34702/run-miniasm-assembler-on-nanopore-reads Mon, 18 Dec 2017 04:07:50 -0600 https://bioinformaticsonline.com/pages/view/34702/run-miniasm-assembler-on-nanopore-reads <![CDATA[Run miniasm assembler on nanopore reads !]]> Miniasm is a very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final unitig sequences. Thus the per-base error rate is similar to the raw input reads.

Find the detail of the reads repeats:

fq2fa ONT_A.fastq ONT_A.fasta 

minimap2 -xava-ont ONT_A.fasta ONT_A.fasta -t10 -X > AONT.paf 

awk '{if($1==$6){print}}' AONT.paf > AONTself.paf 

awk '$5=="-"' AONTself.paf | awk '{print $1}'| sort|uniq > invertedrepeat.list

Generated a few palindrome and repeats plots (highlighting only repeats largest than 10, 20 and 30 kb)

minidot -f 5 -m 30000 AONTself.paf > AONTself30000.eps 
sed 's/_template_pass_FAH31515//' AONTself30000.eps > AONTself30000final.eps 

minidot -f 5 -m 20000 AONTself.paf > AONTself20000.eps 
sed 's/_template_pass_FAH31515//' AONTself20000.eps > AONTself20000final.eps 

minidot -f 5 -m 10000 AONTself.paf > AONTself10000.eps 
sed 's/_template_pass_FAH31515//' AONTself10000.eps > AONTself10000final.eps 

Assemble with miniasm:

miniasm -f ONT_A.fasta AONT.paf > AONT.gfa 

grep '^S' AONT.gfa |awk '{print ">"$2"\n"$3}' > AONT_miniasm.fasta 

minimap2 -xasm10 AONT_miniasm.fasta AONT_miniasm.fasta -t1 -X > AONT_miniasm.paf 

awk '{if($1==$6){print}}' AONT_miniasm.paf > AONT_miniasm_self.paf 

minidot -f 5 -m 10000 AONT_miniasm_self.paf > AONT_miniasm_self10000.eps 

Njoy the assembly !

]]> Jit