<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/11399?offset=1390 https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data Thu, 28 May 2020 21:57:03 -0500 https://bioinformaticsonline.com/bookmarks/view/41730/parliament2-runs-a-combination-of-tools-to-generate-structural-variant-calls-on-whole-genome-sequencing-data <![CDATA[Parliament2: Runs a combination of tools to generate structural variant calls on whole-genome sequencing data]]> Parliament2 identifies structural variants in a given sample relative to a reference genome. These structural variants cover large deletion events that are called as Deletions of a region, Insertions of a sequence into a region, Duplications of a region, Inversions of a region, or Translocations between two regions in the genome.

Parliament2 runs a combination of tools to generate structural variant calls on whole-genome sequencing data. It can run the following callers: Breakdancer, Breakseq2, CNVnator, Delly2, Manta, and Lumpy. Because of synergies in how the programs use computational resources, these are all run in parallel. Parliament2 will produce the outputs of each of the tools for subsequent investigation.

Address of the bookmark: https://github.com/dnanexus/parliament2

]]> Jit https://bioinformaticsonline.com/blog/view/14011/dynamic-chromosome-breakpoints Wed, 13 Aug 2014 18:38:10 -0500 https://bioinformaticsonline.com/blog/view/14011/dynamic-chromosome-breakpoints <![CDATA[Dynamic chromosome breakpoints !!!]]> 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.

image

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.

 

 

image

 

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

]]> Jitendra Narayan https://bioinformaticsonline.com/opportunity/view/43227/project-associate-i-project-associate-ii-senior-project-associate-igib Thu, 05 Aug 2021 16:11:32 -0500 <![CDATA[Project Associate-I | Project Associate-II | Senior Project Associate @ IGIB]]> Experience in Next Generation Sequencing (NGS) application and interest in Genomics/ Clinical / Translational Applications. OR Good computational programming skills and deep interest in working on interface of Genomics and Clinical application.

Project Scientist-I
Experimental / Computation analysis experience in highthroughput genomics/ clinical application.

Project Manager
Experience in handling large biological projects involving high-throughput genomics/ clinical application.

Scientific Administrative Assistant
Lab Work.

More at https://vinodscaria.genomes.in/positionsopen

]]> https://bioinformaticsonline.com/blog/view/44758/the-ifs-and-buts-of-ngs-quality-control-and-trimming Thu, 02 Jan 2025 20:11:07 -0600 https://bioinformaticsonline.com/blog/view/44758/the-ifs-and-buts-of-ngs-quality-control-and-trimming <![CDATA[The "Ifs" and "Buts" of NGS Quality Control and Trimming]]> Next-Generation Sequencing (NGS) has revolutionized biological research, providing vast amounts of data for a wide range of applications. However, the reliability of NGS analyses heavily depends on the quality of raw sequencing data. Quality control (QC) and trimming are critical preprocessing steps that can make or break your downstream analyses. In this blog, we explore the "ifs" (why you should perform QC and trimming) and the "buts" (challenges or considerations) of this vital step in NGS workflows.

The "Ifs" of NGS QC and Trimming

  1. Ensures Data Integrity
    If you want to minimize errors in downstream analyses, QC and trimming remove low-quality reads and bases, ensuring high-confidence data. This step is essential for reliable variant calling, assembly, and other applications.

  2. Removes Contaminants
    If adapter sequences or contaminants are present in the raw reads, trimming can eliminate them. This prevents issues like misalignment or incorrect biological interpretations, ensuring cleaner data for analysis.

  3. Improves Mapping and Assembly
    If your goal is better alignment to a reference genome or improved de novo assembly, trimming low-quality bases and adapters is critical. High-quality reads map more efficiently and generate more accurate assemblies.

  4. Reduces Computational Load
    If you want to save computational resources, trimming reduces the dataset size, which speeds up processing and analysis. Clean datasets mean less computational time spent on processing low-quality data.

  5. Prepares for Standardized Analyses
    If your project involves multiple datasets, QC and trimming ensure uniformity across them. This standardization makes comparisons valid and reproducible, particularly in large collaborative studies.

The "Buts" of NGS QC and Trimming

  1. Risk of Over-Trimming
    But excessive trimming can lead to the loss of informative sequences, reducing read depth and potentially discarding biologically relevant data. This is especially critical in studies with limited sequencing depth.

  2. Bias Introduction
    But trimming algorithms might introduce biases, especially if they inadvertently remove sequences with specific biological patterns. This can skew results and compromise biological insights.

  3. Loss of Context in Paired-End Reads
    But trimming one read in a pair more than the other can lead to loss of pairing information. This complicates downstream analyses that rely on paired-end data, such as structural variant detection.

  4. Time and Resource Intensive
    But running QC and trimming for large datasets can be computationally expensive and time-consuming. As sequencing depth increases, preprocessing becomes a bottleneck in the analysis pipeline.

  5. Variable Standards
    But the criteria for trimming (e.g., quality threshold, minimum read length) can vary between tools and datasets. This variability may affect reproducibility and comparability of results across studies.

Balancing the "Ifs" and "Buts"

To maximize the benefits of QC and trimming while mitigating the challenges, consider the following best practices:

  • Use QC Tools Wisely: Start with tools like FastQC to identify quality issues in your raw data. Visualizing quality metrics helps tailor your trimming parameters.

  • Choose Reliable Trimming Tools: Tools like Trimmomatic, Cutadapt, and BBduk offer adaptive and customizable trimming options. Select one that aligns with your dataset and project goals.

  • Set Reasonable Parameters: Avoid over-trimming by setting quality thresholds and minimum read lengths that balance data retention and quality improvement.

  • Test Downstream Effects: Validate the impact of QC and trimming on downstream analyses, such as alignment efficiency, variant calling accuracy, or assembly quality.

  • Document Your Workflow: Maintain detailed records of the parameters and tools used for QC and trimming. This ensures reproducibility and enables better troubleshooting.

Conclusion

NGS quality control and trimming are essential steps to ensure reliable and accurate data for analysis. While the "ifs" highlight the clear benefits of these steps, the "buts" remind us of the potential pitfalls. By adopting best practices and carefully balancing these considerations, you can optimize your preprocessing workflow and unlock the full potential of your sequencing data.

]]> BioStar https://bioinformaticsonline.com/pages/view/37411/my-commonly-used-commands-in-bioinformatics Thu, 26 Jul 2018 04:58:45 -0500 https://bioinformaticsonline.com/pages/view/37411/my-commonly-used-commands-in-bioinformatics <![CDATA[My commonly used commands in Bioinformatics]]> FYI, I've found it useful to use MUMmer to extract the specific changes that Racon makes, so I can evaluate them individually:

minimap -t 24 assembly.fasta long_reads.fastq.gz | racon -t 24 long_reads.fastq.gz - assembly.fasta racon_assembly.fasta
nucmer -p nucmer assembly.fasta racon_assembly.fasta
show-snps -C -T -r nucmer.delta

This reports Racon's changes in a table. You can exclude indels with the -I option in show-snps. 

This process (Racon -> MUMmer -> SNP table) solves the problem I originally raised in this issue. So as far as I'm concerned, you can close this issue (or keep it open if you still want to implement some kind of variant table).

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/38063/referee-genome-assembly-quality-scores Sun, 04 Nov 2018 16:44:30 -0600 https://bioinformaticsonline.com/bookmarks/view/38063/referee-genome-assembly-quality-scores <![CDATA[Referee: Genome assembly quality scores]]> Modern genome sequencing technologies provide a succint measure of quality at each position in every read, however all of this information is lost in the assembly process. Referee summarizes the quality information from the reads that map to a site in an assembled genome to calculate a quality score for each position in the genome assembly.

We accomplish this by first calculating genotype likelihoods for every site. For a given site in a diploid genome, there are 10 possible genotypes (AA, AC, AG, AT, CC, CG, CT, GG, GT, TT). Referee takes as input the genotype likelihoods calculated for all 10 genotypes given the called reference base at each position.

Referee is a program to calculate a quality score for every position in a genome assembly. This allows for easy filtering of low quality sites for any downstream analysis.

https://github.com/gwct/referee

Address of the bookmark: https://gwct.github.io/referee/#

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39726/jackalope-a-swift-versatile-phylogenomic-and-high-throughput-sequencing-simulator Fri, 26 Jul 2019 00:58:12 -0500 https://bioinformaticsonline.com/bookmarks/view/39726/jackalope-a-swift-versatile-phylogenomic-and-high-throughput-sequencing-simulator <![CDATA[jackalope: A swift, versatile phylogenomic and high-throughput sequencing simulator]]> jackalope simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations. jackalope can simulate single, paired-end, or mate-pair Illumina reads, as well as reads from Pacific Biosciences These simulations include sequencing errors, mapping qualities, multiplexing, and optical/PCR duplicates. All outputs can be written to standard file formats.

A swift, versatile phylogenomic and high-throughput sequencing simulator https://jackalope.lucasnell.com

Address of the bookmark: https://github.com/lucasnell/jackalope

]]> Abhimanyu Singh https://bioinformaticsonline.com/bookmarks/view/40699/kevler-reference-free-variant-discovery-in-large-eukaryotic-genomes Tue, 28 Jan 2020 03:21:53 -0600 https://bioinformaticsonline.com/bookmarks/view/40699/kevler-reference-free-variant-discovery-in-large-eukaryotic-genomes <![CDATA[Kevler: Reference-free variant discovery in large eukaryotic genomes]]> Welcome to kevlar, software for predicting de novo genetic variants without mapping reads to a reference genome! kevlar's k-mer abundance based method calls single nucleotide variants (SNVs), multinucleotide variants (MNVs), insertion/deletion variants (indels), and structural variants (SVs) simultaneously with a single simple model. 

More at https://kevlar.readthedocs.io/en/latest/

https://www.cell.com/iscience/pdf/S2589-0042(19)30259-7.pdf

Address of the bookmark: https://github.com/kevlar-dev/kevlar

]]> Jit https://bioinformaticsonline.com/blog/view/710/how-to-install-perl-modules-manually-using-cpan-command-and-other-quick-ways Fri, 12 Jul 2013 07:20:24 -0500 https://bioinformaticsonline.com/blog/view/710/how-to-install-perl-modules-manually-using-cpan-command-and-other-quick-ways <![CDATA[How to install Perl modules manually, using CPAN command, and other quick ways]]> As a bioinformatics programmer, and crunchy data analyser you need to install several perl modules and dependencies. Installing Perl modules manually by resolving all the dependencies is  tedious and annoying process. Some of the packages like GD is the real pain.

However, Installing Perl modules using CPAN is a better solution, as it resolves all the dependencies automatically. In this article, let us review how to install Perl modules on Linux ( which is prefereced amonst bioinformatician) using both manual and CPAN method.

When a Perl module is not installed, application will display the following error message. In this example, XML::Parser Perl module is missing.

Can't locate XML/parser.pm in @INC (@INC contains:
/usr/lib/perl5/5.10.0/i386-linux-thread-multi
/usr/lib/perl5/5.10.0
/usr/local/lib/perl5/site_perl/5.10.0/i386-linux-thread-multi
/usr/local/lib/perl5/site_perl/5.10.0
/usr/lib/perl5/vendor_perl/5.10.0/i386-linux-thread-multi
/usr/lib/perl5/vendor_perl/5.10.0 /usr/lib/perl5/vendor_perl
/usr/lib/perl5/site_perl/5.10.0 .)

Manual Method of Perl Module Installation

  • Install Perl Modules Manually

This manual method is very useful when your computer or server is not connected to the Internet.

Download Perl module:
Go to CPAN Search website and search for the module that you wish to download. In this example, let us search, download and install XML::Parser Perl module. I have downloaded the XML-Parser-2.36.tar.gz to /home/download

# cd /home/download
# gzip -d XML-Parser-2.36.tar.gz
# tar xvf XML-Parser-2.36.tar
# cd XML-Parser-2.36

Build the perl module:
Build by running Makefile.PL, remember the case sensitivity, make and make test.

# perl Makefile.PL
Checking if your kit is complete...
Looks good
Writing Makefile for XML::Parser::Expat
Writing Makefile for XML::Parser
# make
# make test

Install the perl module:
Now your package is ready to install.

# make install

As a newbie it looks pretty simple, and one go. But, luckily this is a very simple one module with no dependencies. Typically, Perl modules will be dependent on several other modules. Just imagine chasing all these dependencies one-by-one, thinking ... oh ye I got it. That will be very painful and annoying task. I recommend the CPAN method of installation as shown below.

Install Perl Modules using CPAN automatically

Logically, you should must have the CPAN perl module installed in your server or computer before you can install any other Perl modules using CPAN. I know you  are laughing, "to install a perl module you need another perl module"  ;)

Lets verify whether CPAN is already installed:

To install Perl modules using CPAN, make sure the cpan command is working. Following are the error message when CPAN module is not installed.

# cpan
-bash: cpan: command not found

# perl -MCPAN -e shell
Can't locate CPAN.pm in @INC (@INC contains:
/usr/lib/perl5/5.10.0/i386-linux-thread-multi
/usr/lib/perl5/5.10.0
/usr/local/lib/perl5/site_perl/5.10.0/i386-linux-thread-multi
/usr/local/lib/perl5/site_perl/5.10.0
/usr/lib/perl5/vendor_perl/5.10.0/i386-linux-thread-multi
/usr/lib/perl5/vendor_perl/5.10.0
/usr/lib/perl5/vendor_perl /usr/lib/perl5/site_perl/5.10.0 .).
BEGIN failed--compilation aborted.

Install the CPAN module using yum:
If CPAN in not installed in your system, you can use "yum" for the rescue. Dont worry biological data cruncher, this is true we are now dependent all these tiny magicians :).

# yum install perl-CPAN

Output of yum install perl-CPAN command:

Loaded plugins: refresh-packagekit
updates-newkey                       | 2.3 kB     00:00
primary.sqlite.bz2                   | 2.4 MB     00:00
Setting up Install Process
Parsing package install arguments

Resolving Dependencies
Transaction Summary
=============================================================================
Install      5 Package(s)
Update       0 Package(s)
Remove       0 Package(s)

Total download size: 1.0 M
Is this ok [y/N]: y
Downloading Packages:
(1/5): perl-ExtUtils-ParseXS-2.18-31.fc9.i386.rpm     |  30 kB     00:00
(2/5): perl-Test-Harness-2.64-31.fc9.i386.rpm         |  70 kB     00:00
(3/5): perl-CPAN-1.9205-31.fc9.i386.rpm               | 217 kB     00:00
(4/5): perl-ExtUtils-MakeMaker-6.36-31.fc9.i386.rpm   | 284 kB     00:00
(5/5): perl-devel-5.10.0-31.fc9.i386.rpm              | 408 kB     00:00

Installing     : perl-ExtUtils-ParseXS                             [1/5]
Installing     : perl-devel                                        [2/5]
Installing     : perl-Test-Harness                                 [3/5]
Installing     : perl-ExtUtils-MakeMaker                           [4/5]
Installing     : perl-CPAN                                         [5/5]


Installed: perl-CPAN.i386 0:1.9205-31.fc9
Dependency Installed:
  perl-ExtUtils-MakeMaker.i386 0:6.36-31.fc9
  perl-ExtUtils-ParseXS.i386 1:2.18-31.fc9
  perl-Test-Harness.i386 0:2.64-31.fc9
  perl-devel.i386 4:5.10.0-31.fc9
Complete!

Configure cpan the first time:
Once the CPAN is installed, you need to configure it by executing cpan, you should set some configuration parameters as shown below. I have shown only the important configuration parameters below. Accept all the default values by pressing enter.

Note: Make sure to execute “o conf commit” in the cpan prompt after the configuration to save the settings.

# cpan

Sorry, we have to rerun the configuration dialog for CPAN.pm due
to some missing parameters...

CPAN build and cache directory? [/root/.cpan]
Download target directory? [/root/.cpan/sources]
Directory where the build process takes place? [/root/.cpan/build]

Always commit changes to config variables to disk? [no]
Cache size for build directory (in MB)? [100]
Let the index expire after how many days? [1]

Perform cache scanning (atstart or never)? [atstart]
Cache metadata (yes/no)? [yes]
Policy on building prerequisites (follow, ask or ignore)? [ask]

Parameters for the 'perl Makefile.PL' command? []
Parameters for the 'perl Build.PL' command? []

Your ftp_proxy? []
Your http_proxy? []
Your no_proxy? []
Is it OK to try to connect to the Internet? [yes]

First, pick a nearby continent and country by typing in the number(s)
(1) Africa
(2) Asia
(3) Central America
(4) Europe
(5) North America
(6) Oceania
(7) South America
Select your continent (or several nearby continents) [] 5

(1) Bahamas
(2) Canada
(3) Mexico
(4) United States
Select your country (or several nearby countries) [] 4

(2) ftp://carroll.cac.psu.edu/pub/CPAN/
(3) ftp://cpan-du.viaverio.com/pub/CPAN/
(4) ftp://cpan-sj.viaverio.com/pub/CPAN/
(5) ftp://cpan.calvin.edu/pub/CPAN
(6) ftp://cpan.cs.utah.edu/pub/CPAN/
e.g. '1 4 5' or '7 1-4 8' [] 2-16

cpan[1]> o conf commit
commit: wrote '/usr/lib/perl5/5.10.0/CPAN/Config.pm'

cpan[2]> quit
No history written (no histfile specified).
Lockfile removed.

  • Install Perl Modules using CPAN

Hey smile please, now you are ready with CPAN and can download modules in one line command.

You can use one of the following method to install a Perl module using cpan:

# perl -MCPAN -e 'install Bundle::BioPerl'

(or)

# cpan
cpan shell -- CPAN exploration and modules installation (v1.9205)
ReadLine support available (maybe install Bundle::CPAN or Bundle::CPANxxl?)

cpan[1]> install "Bundle::BioPerl"

In the example above, CPAN will check for Bundle::BioPerl dependencies and automatically resolves and installs Bundle::BioPerl with all the dependent Perl modules.

  • Quick Ways

Oh, look at your face.. smily hmm :). This is what your are looking for, a quick and best way to install Perl modules, Bioperl. Following are the the steps to download BioPerl in your server/computer.

# sudo apt-cache search perl BioPerl

Output will be like as follows:

bioperl - Perl tools for computational molecular biology
bioperl-run - BioPerl wrappers: scripts
libbio-perl-perl - BioPerl core perl modules
libbio-perl-run-perl - BioPerl wrappers: modules
libbio-samtools-perl - Perl interface to SamTools library for DNA sequencing
libbiojava-java - Java API to biological data and applications (default version)
libbiojava3-java - Java API to biological data and applications (default version)
python-biopython-sql - Biopython support for the BioSQL database schema
libbtlib-perl - library for basic sequence manipulation

# sudo apt-get install bioperl

If it is installed then flash the following message:

Reading package lists... Done
Building dependency tree      
Reading state information... Done
bioperl is already the newest version.
0 upgraded, 0 newly installed, 0 to remove and 10 not upgraded.

In it is found not installed in your server or system them install all with dependencies.

You can use the same approach to install all the modules, and packages if required.

Thanks for reading. Best of luck for your research.

]]> Jit https://bioinformaticsonline.com/news/view/1178/r-package-for-visualising-go-enrichment Mon, 22 Jul 2013 12:25:09 -0500 https://bioinformaticsonline.com/news/view/1178/r-package-for-visualising-go-enrichment <![CDATA[R package for visualising GO enrichment]]> An R package that visualizes the GO enrichment results as word clouds and arranges them together with figures of experimental data. This allows us to draw informative summary plots for analyses such as differential expression or clustering, where for each gene list we display its behaviour in the experiment alongside with its GO annotations.

Links @ http://raivokolde.github.io/GOsummaries/

Lab @ http://biit.cs.ut.ee/about/main

]]> Jit