<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/42303?offset=20 https://bioinformaticsonline.com/bookmarks/view/41669/filtlong-quality-filtering-tool-for-long-reads Wed, 13 May 2020 10:23:55 -0500 https://bioinformaticsonline.com/bookmarks/view/41669/filtlong-quality-filtering-tool-for-long-reads <![CDATA[Filtlong: quality filtering tool for long reads]]> Filtlong is a tool for filtering long reads by quality. It can take a set of long reads and produce a smaller, better subset. It uses both read length (longer is better) and read identity (higher is better) when choosing which reads pass the filter.

Filtlong builds into a stand-alone executable:

git clone https://github.com/rrwick/Filtlong.git
cd Filtlong
make -j
bin/filtlong -h

Address of the bookmark: https://github.com/rrwick/Filtlong

]]> Radha Agarkar https://bioinformaticsonline.com/news/view/3854/user-dashboard Sat, 24 Aug 2013 05:13:10 -0500 https://bioinformaticsonline.com/news/view/3854/user-dashboard <![CDATA[User Dashboard]]> Added a new feature "User Dashboard" that lets you track the activity and content on this site that matters to you.

Now you can customized your BOL website view on your own dashboard.

Cheers

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/26968/scalce Fri, 15 Apr 2016 05:09:51 -0500 https://bioinformaticsonline.com/bookmarks/view/26968/scalce <![CDATA[SCALCE]]> SCALCE (/skeɪlz/, a.k.a. boosting Sequence Compression Algorithms using Locally ConsistentEncoding) is a tool for compressing FASTQ files. It is designed specifically for the Illumina-generated FASTQ files, but supports any valid FASTQ with consistent read lengths. 

More at http://sfu-compbio.github.io/scalce/

Address of the bookmark: http://sfu-compbio.github.io/scalce/

]]> Surabhi Chaudhary https://bioinformaticsonline.com/bookmarks/view/27261/segemehl Tue, 10 May 2016 08:10:15 -0500 https://bioinformaticsonline.com/bookmarks/view/27261/segemehl <![CDATA[segemehl]]> segemehl is a software to map short sequencer reads to reference genomes. Unlike other methods, segemehl is able to detect not only mismatches but also insertions and deletions. Furthermore, segemehl is not limited to a specific read length and is able to map primer- or polyadenylation contaminated reads correctly.  segemehl implements a matching strategy based on enhanced suffix arrays (ESA). 

More at http://www.bioinf.uni-leipzig.de/Software/segemehl/

Manual http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_manual_0_1_7.pdf

Address of the bookmark: http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html

]]> Anjana 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/35144/converting-fastq-to-fasta Fri, 12 Jan 2018 03:49:09 -0600 https://bioinformaticsonline.com/pages/view/35144/converting-fastq-to-fasta <![CDATA[Converting FASTQ to FASTA]]>

There are several ways you can convert fastq to fasta sequences. Some methods are listed below.

Using SED

sed can be used to selectively print the desired lines from a file, so if you print the first and 2rd line of every 4 lines, you get the sequence header and sequence needed for fasta format.

sed -n '1~4s/^@/>/p;2~4p' INFILE.fastq > OUTFILE.fasta

Using PASTE

You can linerize every 4 lines in a tabular format and print first and second field using paste

cat INFILE.fastq | paste - - - - |cut -f 1, 2| sed 's/@/>/'g | tr -s "/t" "/n" > OUTFILE.fasta

EMBOSS:seqret

Standard script that can be used for many purposes. One such use is fastq-fasta conversion

seqret -sequence reads.fastq -outseq reads.fasta

awk can be used for conversion as follows:

Using AWK

cat infile.fq | awk '{if(NR%4==1) {printf(">%s\n",substr($0,2));} else if(NR%4==2) print;}' > file.fa

FASTX-toolkit

fastq_to_fasta is available in the FASTX-toolkit that scales really well with the huge datasets

fastq_to_fasta -h
usage: fastq_to_fasta [-h] [-r] [-n] [-v] [-z] [-i INFILE] [-o OUTFILE]
# Remember to use -Q33 for illumina reads!
version 0.0.6
       [-h]         = This helpful help screen.
       [-r]         = Rename sequence identifiers to numbers.
       [-n]         = keep sequences with unknown (N) nucleotides.
                   Default is to discard such sequences.
       [-v]         = Verbose - report number of sequences.
                   If [-o] is specified,  report will be printed to STDOUT.
                   If [-o] is not specified (and output goes to STDOUT),
                   report will be printed to STDERR.
       [-z]         = Compress output with GZIP.
       [-i INFILE]  = FASTA/Q input file. default is STDIN.
       [-o OUTFILE] = FASTA output file. default is STDOUT.

Bioawk

Another option to convert fastq to fasta format using bioawk

bioawk -c fastx '{print ">"$name"\n"$seq}' input.fastq > output.fasta

Seqtk

From the same developer, there is another option using a tool called seqtk

seqtk seq -a input.fastq > output.fasta

Note that you can use either compressed or uncompressed files for this tool

]]> Neel https://bioinformaticsonline.com/bookmarks/view/33789/i-pv-interactive-protein-sequence-visualization Mon, 03 Jul 2017 07:52:51 -0500 https://bioinformaticsonline.com/bookmarks/view/33789/i-pv-interactive-protein-sequence-visualization <![CDATA[I-PV: Interactive Protein Sequence Visualization]]> I-PV is a interactive data visualization software designed for inspection of protein sequences and mutation information. It is mainly used for Genetics and Bioinformatics. So what exactly makes it standout?

http://i-pv.org/ipv_rec

Address of the bookmark: http://i-pv.org/

]]> Jit https://bioinformaticsonline.com/blog/view/37049/chromomap-an-r-package-for-interactive-visualization-and-mapping-of-human-chromosomes Mon, 25 Jun 2018 17:22:24 -0500 https://bioinformaticsonline.com/blog/view/37049/chromomap-an-r-package-for-interactive-visualization-and-mapping-of-human-chromosomes <![CDATA[chromoMap-An R package for Interactive visualization and mapping of human chromosomes]]> chromoMap is an R package that provides interactive, configurable and elegant graphics visualization of the human chromosomes allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the enormous size of the chromosomes, it is impractical to visualize each element on the same plot. But chromoMap plots provide a magnified view for each of chromosome location to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order (You will see in the demos below). Not ony that, the plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

https://cran.r-project.org/web/packages/chromoMap/index.html

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/43243/interactive-bioinformatics-resources Thu, 12 Aug 2021 00:09:00 -0500 https://bioinformaticsonline.com/bookmarks/view/43243/interactive-bioinformatics-resources <![CDATA[Interactive Bioinformatics Resources !]]> Learn how to use bioinformatics tools right from your browser.
Everything runs in a sandbox, so you can experiment all you want.

More at sandbox.bio

Address of the bookmark: http://sandbox.bio

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37788/s-plot2-creates-an-interactive-two-dimensional-heatmap-of-sequences Fri, 28 Sep 2018 05:36:19 -0500 https://bioinformaticsonline.com/bookmarks/view/37788/s-plot2-creates-an-interactive-two-dimensional-heatmap-of-sequences <![CDATA[S-plot2: creates an interactive, two-dimensional heatmap of sequences]]> S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.

http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf

http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot

]]> Jit