BOL: Related items

FastProNGS: fast preprocessing of next-generation sequencing reads

Rahul Nayak — Sat, 26 Dec 2020 08:35:21 -0600

FastProNGS to integrate the quality control process with automatic adapter removal. Parallel processing was implemented to speed up the process by allocating multiple threads. Compared with similar up-to-date preprocessing tools, FastProNGS is by far the fastest.

Address of the bookmark: https://github.com/Megagenomics/FastProNGS

Understanding kmer !

BioStar — Wed, 18 Aug 2021 04:27:51 -0500

What is a k-mer anyway? A k-mer is just a sequence of k characters in a string (or nucleotides in a DNA sequence). Now, it is important to remember that to get all k-mers from a sequence you need to get the first k characters, then move just a single character for the start of the next k-mer and so on. Effectively, this will create sequences that overlap in k-1 positions.

Address of the bookmark: https://bioinfologics.github.io/post/2018/09/17/k-mer-counting-part-i-introduction/

Stacks

Jitendra Narayan — Wed, 24 Feb 2016 15:52:30 -0600

Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography.

More at http://catchenlab.life.illinois.edu/stacks/

Address of the bookmark: http://catchenlab.life.illinois.edu/stacks/

cutadapt

Radha Agarkar — Fri, 13 May 2016 04:54:50 -0500

Cutadapt finds and removes adapter sequences, primers, poly-A tails and other types of unwanted sequence from your high-throughput sequencing reads.

Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3’ sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling out RNA from your sample, but often you don’t want them to be in your reads.

Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.

If you use cutadapt, please cite DOI:10.14806/ej.17.1.200 .

Address of the bookmark: https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart

WgSim

Jit — Thu, 23 Jun 2016 07:26:49 -0500

Reads simulator

Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.

Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.

Address of the bookmark: https://github.com/lh3/wgsim

BIMA V3: an aligner customized for mate pair library sequencing

Abhimanyu Singh — Wed, 14 Dec 2016 15:20:00 -0600

Summary: Mate pair library sequencing is an effective and economical method for detecting genomic structural variants and chromosomal abnormalities. Unfortunately, the mapping and alignment of mate pair read pairs to a reference genome is a challenging and
time consuming process for most NGS alignment programs. Large insert sizes, introduction of library preparation protocol artifacts (biotin junction reads, paired-end read contamination, chimeras, etc.), and presence of structural variant breakpoints within reads increases mapping and alignment complexity. We describe an algorithm that is up to 20 times faster and 25% more accurate than popular NGS alignment programs when processing mate pair sequencing.
Availability: http://bioinformaticstools.mayo.edu/research/bima/
Contact: vasmatzis.george@mayo.edu

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf

Software and Tools to detect structure variation with long reads !!

Archana Malhotra — Wed, 15 Mar 2017 14:31:09 -0500

Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio de novo assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.

With Single Molecule, Real-Time (SMRT) Sequencing, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:

Uncover missing heritability linked to structural variation
Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease
Resolve structural variation across the complete size spectrum with basepair resolution

Following are the SV tools, which can assist you to achieve your goal.

Sniffles: Structural variation caller using third generation sequencing

Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!

More at https://github.com/fritzsedlazeck/Sniffles

MultiBreak-SV: It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.

There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.

More at https://github.com/raphael-group/multibreak-sv

Parliament: A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?

Publication about the algorithm and “…the first long-read characterization of structural variation in a diploid human personal genome…” (HS1011) - “Assessing structural variation in a personal genome—towards a human reference diploid genome”

More at https://sourceforge.net/projects/parliamentsv/

https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf

PBHoney: the structural variation discovery tool

PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

Read The Paper http://www.biomedcentral.com/1471-2105/15/180/abstract

More at https://sourceforge.net/projects/pb-jelly/

SMRT-SV: Structural variant and indel caller for PacBio reads

Structural variant (SV) and indel caller for PacBio reads based on methods from Chaisson et al. 2014.

SMRT-SV provides an official software package for tools described in Chaisson et al. 2014 and adds several key features including the following.

Unified variant calling user interface with built-in cluster compute support
Small indel calling (2-49 bp)
Improved inversion calling (screenInversions)
Quality metric for SV calls based on number of local assemblies supporting each call
Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions
Genotyping of SVs with Illumina paired-end reads from WGS samples

More at https://github.com/EichlerLab/pacbio_variant_caller

Common steps for reads mapping !

BioStar — Thu, 09 Mar 2023 02:48:02 -0600

Mapping reads to a reference genome is an essential step in many types of genomic analysis, such as variant calling and gene expression analysis. Here are some general steps to follow for mapping reads to a genome:

Choose a read mapper: There are many read mappers available, such as BWA, Bowtie, and HISAT2. Choose a mapper that is appropriate for your type of data and research question.
Index the reference genome: Before mapping reads, the reference genome needs to be indexed. This involves creating an index of the genome sequence that allows the mapper to quickly find matches to the reads. Most mappers have their own indexing tools.
Prepare the read data: The reads should be in a format that is compatible with the mapper. Most mappers accept FASTQ or BAM files. Depending on the quality of the data, it may need to be filtered or trimmed before mapping.
Run the mapper: The mapper is run with the command-line interface or using a graphical user interface. The specific command depends on the mapper being used, but typically involves specifying the input data, reference genome, and output file format.
Evaluate the mapping results: After the mapping is complete, the results should be evaluated. This includes assessing the quality of the mapping, such as the mapping rate, the number of mapped reads, and the mapping quality score.
Post-processing: Depending on the analysis being performed, post-processing of the mapped reads may be necessary. This can include filtering reads based on quality, removing duplicate reads, and calling variants.

Overall, mapping reads to a reference genome is a complex process that requires careful consideration of the type of data, the research question, and the specific mapper being used.

ONT assembly and Illumina polishing pipeline

Jit — Thu, 23 Nov 2017 10:13:42 -0600

This pipeline performs the following steps:

Assembly of nanopore reads using Canu.
Polish canu contigs using racon (optional).
Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using BWA mem.
Perform correction of contigs using pilon and the Illumina dataset.

Address of the bookmark: https://github.com/nanoporetech/ont-assembly-polish

dnaPipeTE: de-novo assembly & annotation Pipeline for Transposable Elements

Rahul Nayak — Sat, 02 Dec 2017 18:25:44 -0600

dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (< 1X).

dnaPipeTE is developped by Clément Goubert, Laurent Modolo and the TREEP team of the LBBE: http://lbbe.univ-lyon1.fr/-Equipe-Elements-transposables-.html?lang=en
You can find the original publication in GBE here: https://academic.oup.com/gbe/article/7/4/1192/533768

output examples of quantification and TE landscape (relative age) produced by dnaPipeTE

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE