<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/32905?offset=180 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/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads Wed, 15 Mar 2017 14:31:09 -0500 https://bioinformaticsonline.com/blog/view/31566/software-and-tools-to-detect-structure-variation-with-long-reads <![CDATA[Software and Tools to detect structure variation with long reads !!]]> 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

]]> Archana Malhotra https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads Mon, 20 Aug 2018 14:17:05 -0500 https://bioinformaticsonline.com/bookmarks/view/37563/colormap-correcting-long-reads-by-mapping-short-reads <![CDATA[CoLoRMap: Correcting Long Reads by Mapping short reads]]> Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads.We introduce CoLoRMap, a hybrid method for correcting noisy long reads, such as the ones produced by PacBio sequencing technology, using high-quality Illumina paired-end reads mapped onto the long reads. Our algorithm is based on two novel ideas: using a classical shortest path algorithm to find a sequence of overlapping short reads that minimizes the edit score to a long read and extending corrected regions by local assembly of unmapped mates of mapped short reads. Our results on bacterial, fungal and insect data sets show that CoLoRMap compares well with existing hybrid correction methods.The source code of CoLoRMap is freely available for non-commercial use at https://github.com/sfu-compbio/colormap

ehaghshe@sfu.ca or cedric.chauve@sfu.ca

Address of the bookmark: https://github.com/sfu-compbio/colormap

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34922/camsa-a-tool-for-comparative-analysis-and-merging-of-scaffold-assemblies Thu, 28 Dec 2017 09:10:26 -0600 https://bioinformaticsonline.com/bookmarks/view/34922/camsa-a-tool-for-comparative-analysis-and-merging-of-scaffold-assemblies <![CDATA[CAMSA :: a tool for Comparative Analysis and Merging of Scaffold Assemblies]]> CAMSA – is a tool for Comparative Analysis and Merging of Scaffold Assemblies, distributed both as a standalone software package and as Python library under the MIT license.

Main features:

  1. works with any number of scaffold assemblies in de-novo non-progressive fashion
  2. allows to simultaneously work with scaffold assemblies obtained from any in silico and in vitro techniques, supporting multiple existing formats via built-in converters
  3. creates an extensive report with several comparative quality metrics (both on assembly level and on the level of individual assembly points)
  4. constructs a merged combined scaffold assembly
  5. provides an interactive framework for a visual comparative analysis of the given assemblies

Address of the bookmark: https://cblab.org/camsa/

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments Mon, 27 Jun 2016 11:01:44 -0500 https://bioinformaticsonline.com/bookmarks/view/28119/kraken-ultrafast-metagenomic-sequence-classification-using-exact-alignments <![CDATA[Kraken: ultrafast metagenomic sequence classification using exact alignments]]> Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.

Krona

https://sourceforge.net/p/krona/home/krona/

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053813/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/37257/asar-advanced-metagenomic-sequence-analysis-in-r Mon, 09 Jul 2018 05:20:50 -0500 https://bioinformaticsonline.com/bookmarks/view/37257/asar-advanced-metagenomic-sequence-analysis-in-r <![CDATA[ASAR: Advanced metagenomic Sequence Analysis in R]]> An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.

To read the manual, please click the link https://askarbek-orakov.github.io/ASAR/

Address of the bookmark: https://github.com/Askarbek-orakov/ASAR

]]> Jit https://bioinformaticsonline.com/bookmarks/view/33011/grinder-biogrinder-a-versatile-omics-shotgun-and-amplicon-sequencing-read-simulator Wed, 24 May 2017 08:41:41 -0500 https://bioinformaticsonline.com/bookmarks/view/33011/grinder-biogrinder-a-versatile-omics-shotgun-and-amplicon-sequencing-read-simulator <![CDATA[Grinder / Biogrinder - A versatile omics shotgun and amplicon sequencing read simulator]]> Grinder is a versatile program to create random shotgun and amplicon sequence libraries based on DNA, RNA or proteic reference sequences provided in a FASTA file.

Grinder can produce genomic, metagenomic, transcriptomic, metatranscriptomic, proteomic, metaproteomic shotgun and amplicon datasets from current sequencing technologies such as Sanger, 454, Illumina. These simulated datasets can be used to test the accuracy of bioinformatic tools under specific hypothesis, e.g. with or without sequencing errors, or with low or high community diversity. Grinder may also be used to help decide between alternative sequencing methods for a sequence-based project, e.g. should the library be paired-end or not, how many reads should be sequenced.

Address of the bookmark: https://sourceforge.net/projects/biogrinder/files/biogrinder/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/43048/coverm-read-coverage-calculator-for-metagenomics Thu, 29 Apr 2021 23:39:14 -0500 https://bioinformaticsonline.com/bookmarks/view/43048/coverm-read-coverage-calculator-for-metagenomics <![CDATA[CoverM: Read coverage calculator for metagenomics]]> CoverM aims to be a configurable, easy to use and fast DNA read coverage and relative abundance calculator focused on metagenomics applications.

CoverM calculates coverage of genomes/MAGs coverm genome (help) or individual contigs coverm contig (help). Calculating coverage by read mapping, its input can either be BAM files sorted by reference, or raw reads and reference genomes in various formats.

Address of the bookmark: https://github.com/wwood/CoverM

]]> Neel https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus Fri, 05 Jan 2018 04:12:20 -0600 https://bioinformaticsonline.com/bookmarks/view/35061/proovread-large-scale-high-accuracy-pacbio-correction-through-iterative-short-read-consensus <![CDATA[proovread : large-scale high-accuracy PacBio correction through iterative short read consensus]]> proovread : large-scale high-accuracy PacBio correction through iterative short read consensus

proovread maps high coverage data to pacbio reads (bwa mem, blasr, daligner) in multiple iterations.

Address of the bookmark: https://github.com/BioInf-Wuerzburg/proovread

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39190/chipulate-a-python3-framework-to-simulate-read-counts-in-a-chip-seq-experiment Mon, 25 Mar 2019 12:46:47 -0500 https://bioinformaticsonline.com/bookmarks/view/39190/chipulate-a-python3-framework-to-simulate-read-counts-in-a-chip-seq-experiment <![CDATA[ChIPulate: A Python3 framework to simulate read counts in a ChIP-seq experiment]]> ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength.

Address of the bookmark: https://github.com/vishakad/chipulate

]]> Abhimanyu Singh