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

HairSplitter: assembling long reads in an unknown number of haplotypes

BioStar — Wed, 07 Dec 2022 00:13:40 -0600

Pros and cons of HairSplitter Limitations of HairSplitter:

Not very fast: it re-polishes the whole assembly

Limited in the number of haplotypes

Strengths of HairSplitter:

Very modular, can be used with any assembler

Naive: makes no assumption on ploidy, parameter-free

Safe: won’t artificially duplicate contigs

HairSplitter splits collapsed assemblies from “draft” assemblies obtained by any means

HairSplitter can recover haplotypes and distinguish repeated elements

Only needs sequencing reads, potentially error-prone

HairSplitter splits collapsed assemblies from “draft” assemblies obtained by any means

HairSplitter can recover haplotypes and distinguish repeated elements

Only needs sequencing reads, potentially error-prone

Not really available yet (github.com/RolandFaure/HairSplitter)

https://hal.archives-ouvertes.fr/hal-03864075/file/RolandFaure_presentation_SeqBIM_2022.pdf

Address of the bookmark: https://hal.archives-ouvertes.fr/hal-03817928/document

Trimmomatic: A flexible read trimming tool for Illumina NGS data

Jit — Fri, 15 Apr 2016 05:58:53 -0500

Paired End:

java -jar trimmomatic-0.35.jar PE -phred33 input_forward.fq.gz input_reverse.fq.gz output_forward_paired.fq.gz output_forward_unpaired.fq.gz output_reverse_paired.fq.gz output_reverse_unpaired.fq.gz ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

This will perform the following:

Remove adapters (ILLUMINACLIP:TruSeq3-PE.fa:2:30:10)
Remove leading low quality or N bases (below quality 3) (LEADING:3)
Remove trailing low quality or N bases (below quality 3) (TRAILING:3)
Scan the read with a 4-base wide sliding window, cutting when the average quality per base drops below 15 (SLIDINGWINDOW:4:15)
Drop reads below the 36 bases long (MINLEN:36)

More at http://www.usadellab.org/cms/?page=trimmomatic

Address of the bookmark: http://www.usadellab.org/cms/?page=trimmomatic

Stampy

Abhi — Fri, 20 May 2016 19:13:32 -0500

Stampy is a package for the mapping of short reads from illumina sequencing machines onto a reference genome. It's recommended for most workflows, including those for genomic resequencing, RNA-Seq and Chip-seq. Stampy excels in the mapping of reads containing that contain sequence variation relative to the reference, in particular for those containing insertions or deletions.

Address of the bookmark: http://www.well.ox.ac.uk/project-stampy

ART: Set of Simulation Tools

Jit — Thu, 03 Nov 2016 08:28:25 -0500

ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the 1000 Genomes Project . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. VarSim) for evaluating variant calling tools or methods.

Address of the bookmark: http://www.niehs.nih.gov/research/resources/software/biostatistics/art/

LoRDEC: a hybrid error correction program for long, PacBio reads

Jit — Mon, 10 Apr 2017 04:16:09 -0500

LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.

Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.
After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.

Why is LoRDEC different?

It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.
It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.

Address of the bookmark: http://www.atgc-montpellier.fr/lordec/

HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning

Abhimanyu Singh — Tue, 01 Jan 2019 12:01:00 -0600

HECIL—Hybrid Error Correction with Iterative Learning—a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments.

HECIL’s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.

Address of the bookmark: https://github.com/NDBL/HECIL

BEAR: Better Emulation for Artificial Reads

BioStar — Sat, 06 Jul 2024 04:27:53 -0500

Created by Stephen Johnson, Brett Trost, Dr. Jeffrey R. Long, Dr. Anthony Kusalik University of Saskatchewan, Department of Computer Science

BEAR is intended to be an easy-to-use collection of scripts for generating simulated WGS metagenomic reads with read lengths, quality scores, error profiles, and species abundances derived from real user-supplied WGS data.

Address of the bookmark: https://github.com/sej917/BEAR

Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

Jit — Thu, 19 Apr 2018 08:06:03 -0500

Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both end of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html).

Address of the bookmark: https://github.com/meiji-bioinf/heap

Andi

Jit — Fri, 13 May 2016 05:16:35 -0500

This is the andi program for estimating the evolutionary distance between closely related genomes. These distances can be used to rapidly infer phylogenies for big sets of genomes. Because andi does not compute full alignments, it is so efficient that it scales even up to thousands of bacterial genomes.

This readme covers all necessary instructions for the impatient to get andi up and running. For extensive instructions please consult the manual.

More at https://github.com/evolbioinf/andi/

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full