BOL: Related items

Pacbio Long Reads Compatible Software and Tools

Archana Malhotra — Wed, 15 Mar 2017 14:19:01 -0500

The following software packages are known to be compatible with PacBio® data, in addition to PacBio's own SMRT® Analysis suite. All packages are believed to be open source or freely available for non-commercial use. See the individual project sites for up-to-date license information. A separate page lists commercial software.

Know of any other open source software for PacBio data? Email us.

Software categories:

Address of the bookmark: https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software

EyeChrom: Visualizing Chromosome Count Data From Plants

Jit — Tue, 08 Jan 2019 10:20:54 -0600

It's goal is to show chromosmal data per genus. Select the genus, and the plot will show the records found for it in the Chromosome Counts Database. note: Report an issue via Gihub: github.com/roszenil/CCDBcurator and github.com/RodrigoRivero/EyeChrom

https://bsapubs.onlinelibrary.wiley.com/doi/pdf/10.1002/aps3.1207

Address of the bookmark: http://eyechrom.com:3838/EyeChrom/

sockeye

Jit — Fri, 17 Feb 2017 08:51:16 -0600

This sockeye software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.

Address of the bookmark: http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3

InfoGenomeR: Integrative reconstruction of cancer genome karyotypes

Jit — Wed, 05 May 2021 01:02:18 -0500

InfoGenomeR is the Integrative Framework for Genome Reconstruction that uses a breakpoint graph to model the connectivity among genomic segments at the genome-wide scale. InfoGenomeR integrates cancer purity and ploidy, total CNAs, allele-specific CNAs, and haplotype information to identify the optimal breakpoint graph representing cancer genomes.

More at https://www.nature.com/articles/s41467-021-22671-6

Address of the bookmark: https://github.com/dmcblab/InfoGenomeR

Genome Stability Laboratory

Mon, 07 Mar 2016 04:16:32 -0600

The bakers yeast, Saccharomyces cerevisiae is an ideal model organism to understand mechanisms of meiotic chromosome segregation. In S. cerevisiae and in mammals, the majority of meiotic crossovers are formed through a highly conserved MSH4p-MSH5p, MLH1p-MLH3p dependent pathway. We are interested in charactering the role of these complexes in crossover formation and distribution among all homolog pairs. Errors in this process are linked to congenital birth defects in humans such as Down's syndrome.Our laboratory is also interested in understanding the effect of genetic background on mutation rate variation using S. cerevisiae as a model. These studies are relevant for understanding cancer progression, genome evolution and architecture. We use high- throughput genomic methods as well as classical genetics to achieve these aims.

More at http://faculty.iisertvm.ac.in/~nishantkt/index.html

Scalpel

Shruti Paniwala — Wed, 20 Aug 2014 02:07:58 -0500

A team from Cold Spring Harbor Laboratory has released an algorithm, called Scalpel, for finding insertions and deletions in next generation sequencing data sets. Scalpel, which is open source and available for download on SourceForge, outperformed the popular tools GATK HaplotypeCaller and SOAPindel in test runs on both simulated and real whole human exomes.

Like other indel callers, Scalpel works by performing de novo assembly of regions of interest, so that misalignment to the reference genome cannot obscure the presence of an insertion or deletion. Scalpel's innovation is to repeatedly check its assembly before comparing to the reference genome, to account for simple sequence repeats that are a regular source of error in indel calling. When Scalpel assembles an exon, it collects reads that map to that exon (including partial matches), splits them into k-mers, and creates a de Bruijn graph to span the exon; however, if it detects repeats in the map, it iteratively increases the size of the k-mers by one base until the repeats are eliminated. This ensures that the final assembly of the exon is highly accurate while minimizing compute time.

The Cold Spring Harbor team's validation of Scalpel, published over the weekend in Nature Methods, compares Scalpel's performance on a live whole exome against HaplotypeCaller and SOAPindel. The donor is an individual with serious neurological disorders, which may be linked to a high incidence of indels. One thousand indels from this individual's exome, called by one or more of the informatics pipelines, were selected for focused resequencing. This resequencing revealed a 77% true positive rate for Scalpel calls, dramatically better than the rates for either of the competing tools; Scalpel performed especially well with indels longer than five base pairs, a traditional weak point for indel callers.

Finally, the authors demonstrate Scalpel's use on a large set of genetic data from nearly 600 families who donated samples to the Simons Simplex Collection, a project of the Simons Foundation Autism Research Initiative. Scalpel found a very high enrichment for indels in children affected by autism, compared with their unaffected siblings, a pattern that persisted even after excluding common variants.

CANU: Assembling Large Genomes with Single-Molecule Sequencing and Locality Sensitive Hashing.

Jit — Tue, 26 Apr 2016 11:38:10 -0500

Canu is a fork of the Celera Assembler designed for high-noise single-molecule sequencing (such as the PacBio RSII or Oxford Nanopore MinION). The software is currently alpha level, feel free to use and report issues encountered.

Canu is a hierachical assembly pipeline which runs in four steps:

Detect overlaps in high-noise sequences using MHAP
Generate corrected sequence consensus
Trim corrected sequences
Assemble trimmed corrected sequences

Read the documentation

New release https://github.com/marbl/canu/releases

Address of the bookmark: https://github.com/marbl/canu

ORFfinder with smart BLAST

Jit — Tue, 17 May 2016 01:43:15 -0500

ORF Finder

ORFfinder is a graphical analysis tool for finding open reading frames (ORFs). We’ve been working on a few updates, and we’d like to find out what you think about them. Read on to find out what you can do with the new ORFfinder.

Smart BLAST (https://ncbiinsights.ncbi.nlm.nih.gov/2015/07/29/smartblast/)

Select one or a group of ORFs and BLAST several databases at once, and use the newly developed SmartBLAST to verify protein names. Looking for the traditional results from BLAST? They’re there too.

BBMap/BBTools package: Multipurpose tool designed for converting reads or other nucleotide data between different formats.

Jit — Mon, 13 Jun 2016 05:47:21 -0500

Reformatis a member of the BBMap/BBTools package. It is a multipurpose tool designed for converting reads or other nucleotide data between different formats. It supports, and can inter-convert:

fastq
fasta
fasta+qual
sam
scarf (an old Illumina format)
bam (if samtools is installed)
gzip
zip
ascii-33 (sanger)
ascii-64 (old Illumina)
paired files
interleaved files

It is multithreaded and can process data at over 500 megabytes per second, and can accept streams from standard in and write to standard out, allowing it to be easily dropped into the middle of a pipeline for format conversion. Reformat autodetects formats based on file extensions and content, making it very easy to use; and the autodetection can be overridden, allowing flexibility for people who don't like to follow naming conventions, or out-of-spec fastq files with qualities values like -17 or 120.

The program has been gradually expanded, and can now perform various other functions. None of these will break pairing, if the input is paired.

Quality trimming (either or both ends)
Quality filtering
Fixed-length trimming
Generation of histograms (base composition, quality, etc)
Subsampling (to a fraction of input reads, or an exact number of reads or bases)
Changing fasta line-wrapping length
Reverse-complementing (all reads or only read 2)
Adding /1 and /2 suffix to read names
GC-content filtering
Length-filtering
Testing for corrupted interleaved files

Reformat is compatible with any platform that supports Java 1.7 or higher. It also has a bash shellscript for simpler invocation. Typical usage examples:

Reformat fastq into fasta:
reformat.sh in=x.fq out=y.fa

Interleave paired reads:
reformat.sh in1=x1.fq in2=x2.fq out=y.fq

Note - you can actually use a shortcut if paired read files have the same name with a 1 and a 2. This is equivalent to the above command:
reformat.sh in=x#.fq out=y.fq

De-interleave reads:
reformat.sh in=x.fq out1=y1.fq out2=y2.fq

Verify that interleaving appears correct, assuming Illumina namimg conventions:
reformat.sh in=x.fq vint

Convert ASCII-33 to ASCII-64:
reformat.sh in=x.fq out=y.fq qin=33 qout=64

Quality-trim paired reads to Q10 on the left and right ends and discard reads shorter than 50bp after trimming:
reformat.sh in1=x1.fq in2=x2.fq out1=y1.fq out2=y2.fq outsingle=singletons.fq qtrim=rl trimq=10 minlength=50

Subsample 10% of the first 20000 pairs in an interleaved file:
reformat.sh in=x.fq out=y.fq reads=20000 samplerate=0.1 int=t
(in this case "int=t" overrides interleaving autodetection, to ensure reads are treated as pairs)

Pipe in a gzipped sam file and pipe out fasta:
reformat.sh in=stdin.sam.gz out=stdout.fa

Reverse-complement reads:
reformat.sh in=x.fq out=y.fq rcomp

For reformatting a file with very long sequences, Reformat will need more memory; just add the additional flag "-Xmx2g". For example, to change the line-wrapping length on the human genome (which has individual sequences over 200Mbp long) to 70 characters:
reformat.sh -Xmx2g in=HG19.fa.gz out=HG19_wrapped.fa.gz fastawrap=70

For additional functions, please run the shellscript with no arguments, or just read it with a text editor. If you have any questions, please post them in this thread.

For people using a non-bash terminal, you may need to type "bash reformat.sh" instead of just "reformat.sh".
For users of Windows or other platforms that do not support bash shellscripts, replace "reformat.sh" with "java -ea -Xmx200m /path/to/bbmap/current/ jgi.ReformatReads"
for example,
java -ea -Xmx200m C:\bbmap\current\ jgi.ReformatReads in=x.fq out=y.fa

Reformat can be downloaded with BBTools here:
https://sourceforge.net/projects/bbmap/

Mauve: a system for constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion

Jit — Sat, 24 Dec 2016 09:20:53 -0600

Mauve is a system for constructing multiple genome alignments in the presence of large-scale evolutionary events such as rearrangement and inversion. Multiple genome alignments provide a basis for research into comparative genomics and the study of genome-wide evolutionary dynamics.

Mauve has been developed with the idea that a multiple genome aligner should require only modest computational resources. It employs algorithmic techniques that scale well in the lengths of sequences being aligned. For example, a pair of Y. pestis genomes can be aligned in under a minute, while a group of 9 divergent Enterobacterial genomes can be aligned in a few hours. However, the current algorithm’s compute time (progressiveMauve) scales cubically in the number of genomes to align, making it unsuitable for datasets containing more than 50-100 bacterial genomes.

Address of the bookmark: http://darlinglab.org/mauve/mauve.html