BOL: Related items

Illumina reveals first dataset of long reads

Rahul Agarwal — Fri, 23 Aug 2013 06:29:14 -0500

With the help of Moleculo technology , acquired by Illumina releases new service for long reads sequencing i.e., FastTrack Long Reads.

Average read length is around 8,500 base pairs in release dataset. Best thing about this, there is not much effect on cost and quality of data.

You can also check following pages for publications on long reads and more:

http://www.illumina.com/services/long-read-sequencing-service.ilmn

http://blog.basespace.illumina.com/2013/07/22/first-data-set-from-fasttrack-long-reads-early-access-service/

Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications.

Jit — Mon, 28 May 2018 09:41:39 -0500

Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It is optimized for analysis of germline variation in small sets of individuals and somatic variation in tumor/normal sample pairs. Manta discovers, assembles and scores large-scale SVs, medium-sized indels and large insertions within a single efficient workflow.

Address of the bookmark: https://github.com/Illumina/manta

SvABA: Genome-wide detection of structural variants and indels by local assembly

Abhimanyu Singh — Mon, 21 Jan 2019 17:58:56 -0600

SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using VariantBam rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.

Address of the bookmark: https://github.com/walaj/svaba

chromatiblock: Scalable, whole-genome visualisation of structural changes in prokaryotes

BioStar — Sat, 22 Aug 2020 05:17:18 -0500

To create a fresh environment for chromatiblock to run in do:

conda create --name chromatiblock
conda activate chromatiblock
conda install chromatiblock --channel conda-forge --channel bioconda

Then in future to run chromatiblock you can reactivate this environemtn using conda activate chromatiblock

Direct download:

Alternatively you can download and run the script from here.

Address of the bookmark: https://github.com/mjsull/chromatiblock

SVEngine: Allele Specific and Haplotype Aware Structural Variants Simulator

Jit — Sat, 04 Jul 2020 05:52:34 -0500

SVEngine (Structural Variants Engine)

SVEngine is a multi-purpose and self-contained simulator for whole genome scale spike-in of thousands of SV events of various types in both single-sample and matched sample scenarios.
SVEngine takes as input reference contigs in FASTA files, variant meta distribution as specified in META files (see Manual) or specific variant information as specified in VAR files (see Manual) and NEWICK files for specifying clonal phylogenetic trees in cancer.
SVEngine outpus alterred contigs in FASTA files, spiked-in variants in VAR files (see Manual), simulated short read in FASTQ files and aligned short reads in BAM files.

Address of the bookmark: https://bitbucket.org/charade/svengine/src/master/

VG: variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods

Jit — Tue, 28 Jan 2020 03:53:24 -0600

Variation graphs provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:

nodes, which are labeled by sequences and ids
edges, which connect two nodes via either of their respective ends
paths, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges

Address of the bookmark: https://github.com/vgteam/vg

SeqMule: Automated human exome/genome variants detection

Abhimanyu Singh — Tue, 07 Mar 2017 10:12:36 -0600

SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file. SeqMule also has some built-in functions, such as pooling consensus calls from various callers, plotting a Venn diagram showing intersection among different callers, and downloading databases. SeqMule can be used for both Mendelian disease study and cancer genome study.

Address of the bookmark: http://seqmule.openbioinformatics.org/en/latest/