BOL: Related items

VariantBam: Filtering and profiling of next-generational sequencing data using region-specific rules

Rahul Nayak — Thu, 04 Oct 2018 16:30:44 -0500

VariantBam is a tool to extract/count specific sets of sequencing reads from next-generational sequencing files. To save money, disk space and I/O, one may not want to store an entire BAM on disk. In many cases, it would be more efficient to store only those read-pairs or reads who intersect some region around the variant locations. Alternatively, if your scientific question is focused on only one aspect of the data (e.g. breakpoints), many reads can be removed without losing the information relevant to the problem.

Address of the bookmark: https://github.com/broadinstitute/VariantBam

EXCAVATOR: detecting copy number variants from whole-exome sequencing data

Radha Agarkar — Fri, 04 Jan 2019 10:10:48 -0600

EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.

EXCAVATOR is a novel software package for the detection of copy number variants (CNVs) from whole-exome sequencing data.
EXCAVATOR has been published on Genome Biology (http://genomebiology.com/2013/14/10/R120/abstract).

Address of the bookmark: https://sourceforge.net/projects/excavatortool/

nQuire: A statistical framework for ploidy estimation using NGS short-read data

Jit — Thu, 31 Jan 2019 05:12:19 -0600

nQuire implements a set of commands to estimate ploidy level of individuals from species, where recent polyploidization occurred and intraspecific ploidy variation is observed. Specifically, nQuire uses next-generation sequencing data to distinguish between diploids, triploids and tetraploids, on the basis of frequency distributions at variant sites where only two bases are segregating.

For more background see also the publication at BMC Bioinformatics.

https://github.com/clwgg/nQuire

Address of the bookmark: https://github.com/clwgg/nQuire

LoFreq*: A sequence-quality aware, ultra-sensitive variant caller for NGS data

BioStar — Tue, 18 Feb 2020 03:24:22 -0600

LoFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of errors inherent in sequencing (e.g. mapping or base/indel alignment uncertainty), which are usually ignored by other methods or only used for filtering.

https://github.com/CSB5/lofreq

http://csb5.github.io/lofreq/installation/

https://github.com/CSB5/lofreq/tree/master/dist

Address of the bookmark: http://csb5.github.io/lofreq/

Submit your SARS-CoV-2 sequence data to GenBank

Neel — Thu, 09 Apr 2020 18:28:25 -0500

Submit your SARS-CoV-2 sequence data to GenBank and SRA with our new submission landing page. Submission is simple and streamlined *and* there’s a rapid turnaround. https://submit.ncbi.nlm.nih.gov/sarscov2/

Quickly and easily add your SARS-CoV-2 sequence data to the growing public archive with new, special features and support from NCBI. new SARS-CoV-2 sequence submission landing page will help you get started. GenBank submissions are accessioned and released in approximately 1-2 working days, and Sequence Read Archive (SRA) submissions typically processed and released within hours. Submission is simple!

More information is available on NCBI Insights. https://ncbiinsights.ncbi.nlm.nih.gov/2020/04/09/sars-cov2-data-streamlined-submission-rapid-turnaround/

McClintock: Meta-pipeline to identify transposable element insertions using next generation sequencing data

BioStar — Tue, 27 Oct 2020 00:21:18 -0500

an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote, Saccharomyces cerevisiae.

Address of the bookmark: https://github.com/bergmanlab/mcclintock

GraphUnzip: Phases an assembly graph using Hi-C data and/or long reads.

Jit — Fri, 05 Feb 2021 21:22:24 -0600

GraphUnzip, a fast, memory-efficient and accurate tool to unzip assembly graphs into their constituent haplotypes using long reads and/or Hi-C data. As GraphUnzip only connects sequences in the assembly graph that already had a potential link based on overlaps, it yields high-quality gap-less supercontigs. To demonstrate the efficiency of GraphUnzip, we tested it on a simulated diploid Escherichia coli genome, and on two real datasets for the genomes of the rotifer Adineta vaga and the potato Solanum tuberosum. In all cases, GraphUnzip yielded highly continuous phased assemblies.

https://www.biorxiv.org/content/biorxiv/early/2021/02/01/2021.01.29.428779.full.pdf

Address of the bookmark: https://github.com/nadegeguiglielmoni/GraphUnzip

Corona Virus Genome and Data Download !

Abhi — Sun, 12 Dec 2021 23:34:54 -0600

Genes and its related metadata could be found on https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/datasets/coronavirus/genomes/

Orange: Data mining

BioStar — Mon, 13 Mar 2023 12:42:29 -0500

Open source machine learning and data visualization.

Build data analysis workflows visually, with a large, diverse toolbox.

Address of the bookmark: https://orangedatamining.com/

Dynamic chromosome breakpoints !!!

Jitendra Narayan — Wed, 13 Aug 2014 18:38:10 -0500

Cell division involves the distribution of identical genetic material, DNA, to two daughters’ cells. During this process, duplicated deoxyribonucleic acid (DNA) goes through a condensation and decondensation process. This is followed by nuclear envelope dissolution, mitotic spindle assembly, migration of the sister chromatid pairs to the metaphase plate, division and segregation of identical sets of chromosomes into daughter nuclei and nuclear envelope reformation.

The vital metaphase stage of cell division, when the sister chromatids migrated to the centre and lined up in a row, and pulled apart using attached microtubules in such a way that half the DNA ends up in each daughter cell. However, before the mitotic spindle‐mediated movement gets start and pulled DNA apart, the chromosomes are free to undergo recombination which involves the exchange of genetic material either between multiple chromosomes or between different regions of the same chromosome.

During recombination, the precise breakage of each strand, exchange between the strands, and sealing of the resulting recombined molecules happens. The “chromosomal breakpoints” refers to these places where they break. Mostly, this process occurs with a high degree of accuracy at high frequency in both eukaryotic and prokaryotic cells. But occasionally this “break and sealing/ break and reattach” process goes wrong and the reattachment happens in the wrong place which usually create disaster (with few exceptions).These chromosome disaster or abnormalities involve the gain, loss or rearrangement of visible amounts of genetic material during cell division. These abnormalities are of two type, the first one is numerical abnormalities where severe disorders are caused by the loss or gain of whole chromosomes, which affect the copy number of hundreds or even thousands of genes. The second are structural abnormalities which can be unbalanced or balanced. The former are similar to numerical abnormalities in that genetic material is either gained or lost. The natural defects in chromosome segregation are linked to cancer and several genetic diseases (http://en.wikipedia.org/wiki/List_of_genetic_disorders). Therefore, the enzymes involved in regulating cell division are still the attractive drug targets for many diseases.

Apart from certain chromosome abnormalities, these “crossing over” of segments of maternal and paternal chromosomes to form hybrid chromosomes have some evolutionary importance and considered as a driver of genetic variation. Moreover, the chromosome breakage in evolution is considered to be non-random in nature(http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0020014). In addition the study of breakpoint regions and non-breakpoint (stable) regions of chromosomes indicates both the regions evolved in distinctly different ways ( http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/). These breakage may lead to genetic diseases or participate to chromosomal rearranmgnets and contributed in development of new species.

I will try to explain the genome hotspots/Evolutionary Breakpoint Regions(EBRs)/fragile regions/weak fragments/ in my next blog.

Software for recombination detection:

RAT http://cbr.jic.ac.uk/dicks/software/RAT/

Breakpointer https://github.com/ruping/Breakpointer

DRP http://web.cbio.uct.ac.za/~darren/rdp.html

RB-finder http://www.ncbi.nlm.nih.gov/pubmed/18707535

LDhat2.0 http://ldhat.sourceforge.net/LDhat2.0/instructions.shtml

Reference:

http://www.nature.com/scitable/topicpage/genetic-recombination-514#

Image: Wikipedia , sciencelearn.org.nz

Recommended Articles:

http://www.friendshipcircle.org/blog/2012/05/22/13-chromosomal-disorders-youve-never-heard-of/

http://web.udl.es/usuaris/e4650869/docencia/segoncicle/genclin98/recursos_classe_%28pdf%29/revisionsPDF/chromosyndromes.pdf

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775595/table/T2/

http://learn.genetics.utah.edu/content/disorders/chromosomal/

http://www.ncert.nic.in/html/learning_basket/biology/cc&cd.pdf