<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/1897?offset=190 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/researchlabs/view/32713/salzberg-lab Mon, 15 May 2017 05:14:01 -0500 <![CDATA[Salzberg lab]]> We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.

https://salzberg-lab.org/

]]> https://bioinformaticsonline.com/bookmarks/view/34734/smash-an-alignment-free-tool-to-find-and-visualise-rearrangements-between-pairs-of-dna-sequences Thu, 21 Dec 2017 08:26:57 -0600 https://bioinformaticsonline.com/bookmarks/view/34734/smash-an-alignment-free-tool-to-find-and-visualise-rearrangements-between-pairs-of-dna-sequences <![CDATA[SMASH: An alignment-free tool to find and visualise rearrangements between pairs of DNA sequences]]> SMASH is a completely alignment-free method to find and visualise rearrangements between pairs of DNA sequences. The detection is based on relative compression, namely using a FCM, also known as Markov model, of high context order (typically 20). The method has been approached with a tool (also called SMASH). For visualization, SMASH outputs a SVG image, with an ideogram output architecture, where the patterns are represented with several HSV values (only value varies). The following image, illustrating the information maps between human and chimpanzee for the several chromosomes, depicts an example:

ScreenShot

 

 

Address of the bookmark: https://github.com/pratas/smash

]]> Jit https://bioinformaticsonline.com/pages/view/35915/iupac-codes Tue, 13 Mar 2018 05:16:05 -0500 https://bioinformaticsonline.com/pages/view/35915/iupac-codes <![CDATA[IUPAC codes]]> IUPAC codes

DNA:

Nucleotide Code: Base:

---------------- -----

A.................Adenine

C.................Cytosine

G.................Guanine

T (or U)..........Thymine (or Uracil)

R.................A or G

Y.................C or T

S.................G or C

W.................A or T

K.................G or T

M.................A or C

B.................C or G or T

D.................A or G or T

H.................A or C or T

V.................A or C or G

N.................any base . or -............gap

Protein:

Amino Acid Code: Three letter Code: Amino Acid:

---------------- ------------------ -----------

A.................Ala.................Alanine

B.................Asx.................Aspartic acid or Asparagine

C.................Cys.................Cysteine

D.................Asp.................Aspartic Acid

E.................Glu.................Glutamic Acid

F.................Phe.................Phenylalanine

G.................Gly.................Glycine

H.................His.................Histidine

I.................Ile.................Isoleucine

K.................Lys.................Lysine

L.................Leu.................Leucine

M.................Met.................Methionine

N.................Asn.................Asparagine

P.................Pro.................Proline

Q.................Gln.................Glutamine

R.................Arg.................Arginine

S.................Ser.................Serine

T.................Thr.................Threonine

V.................Val.................Valine

W.................Trp.................Tryptophan

X.................Xaa.................Any amino acid

Y.................Tyr.................Tyrosine

Z.................Glx.................Glutamine or Glutamic acid

]]> Neel https://bioinformaticsonline.com/bookmarks/view/36905/d-genies-a-tool-for-dotplot-large-genomes-in-an-interactive-efficient-and-simple-way Mon, 11 Jun 2018 09:41:22 -0500 https://bioinformaticsonline.com/bookmarks/view/36905/d-genies-a-tool-for-dotplot-large-genomes-in-an-interactive-efficient-and-simple-way <![CDATA[D-GENIES: A tool for Dotplot large Genomes in an Interactive, Efficient and Simple way]]> Address of the bookmark: http://dgenies.toulouse.inra.fr/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/39872/miropeats-discovers-regions-of-sequence-similarity-amongst-any-set-of-dna-sequences Mon, 26 Aug 2019 17:55:24 -0500 https://bioinformaticsonline.com/bookmarks/view/39872/miropeats-discovers-regions-of-sequence-similarity-amongst-any-set-of-dna-sequences <![CDATA[Miropeats: discovers regions of sequence similarity amongst any set of DNA sequences]]> Miropeats discovers regions of sequence similarity amongst any set of DNA sequences and then presents this similarity information graphically. Sequence similarity searching is a very general tool that forms the basis of many different biological sequence analyses but it is limited by the verbosity of traditional alignment presentation styles. Miropeats enhances the utility of conventional DNA sequence comparisons when looking at long lengths of sequence similarity by summarizing extensive large scale sequence similarities on a single page of graphics. The latest version of Miropeats can be used as a general pairwise alignment program or in its traditional role sorting out a big mess of overlapping or similar regions.

Address of the bookmark: http://www.littlest.co.uk/software/bioinf/old_packages/miropeats/

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/40865/dminda2-an-integrated-web-server-for-dna-motif-identification-and-analyses Sun, 02 Feb 2020 14:26:01 -0600 https://bioinformaticsonline.com/bookmarks/view/40865/dminda2-an-integrated-web-server-for-dna-motif-identification-and-analyses <![CDATA[DMINDA2: an integrated web server for DNA motif identification and analyses]]> DMINDA (DNA motif identification and analyses) is an integrated web server for DNA motif identification and analyses

More at http://bmbl.sdstate.edu/DMINDA2/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086085/

Address of the bookmark: http://bmbl.sdstate.edu/DMINDA2/

]]> BioStar https://bioinformaticsonline.com/bookmarks/view/44495/exrec-exclusion-of-recombined-dna Wed, 27 Mar 2024 20:48:26 -0500 https://bioinformaticsonline.com/bookmarks/view/44495/exrec-exclusion-of-recombined-dna <![CDATA[ExRec: Exclusion of Recombined DNA]]> ExRec (Exclusion of Recombined DNA) is a Python pipeline that implements the four-gamete test to filter out recombined DNA sites from up to thousands of DNA sequence loci. The pipeline consists of five standalone applications: the first two convert folders of NEXUS or PHYLIP files into the standard input file for the main program that conducts the four-gamete filtering procedures. The pipeline outputs recombination-filtered data in concatenated NEXUS and PHYLIP formats and a tab-delimited table containing descriptive statistics for all loci and the results. This software also allows the user to output the longest non-recombined sequence blocks from loci (current best practice) or randomly select non-recombined blocks from loci (a newer approach). Two other applications in the package convert the recombination-filtered data into single-locus NEXUS or PHYLIP files. The ExRec package can thus facilitate species delimitation, species tree, and historical demography studies by providing loci that better meet the no-recombination assumption in coalescent-based analyses.

Link to the article: https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad174/7455250?searchresult=1

Link to the software:
https://github.com/Sammccarthypotter/ExRec

Address of the bookmark: https://github.com/Sammccarthypotter/ExRec

]]> LEGE https://bioinformaticsonline.com/blog/view/38238/list-of-motif-discovery-tools Tue, 20 Nov 2018 03:54:26 -0600 https://bioinformaticsonline.com/blog/view/38238/list-of-motif-discovery-tools <![CDATA[List of motif discovery tools !]]>
In genetics, a sequence motif is a nucleotide or amino-acid sequence pattern that is widespread and has, or is conjectured to have, a biological significance. For proteins, a sequence motif is distinguished from a structural motif, a motif formed by the three-dimensional arrangement of amino acids which may not be adjacent.
 
Following are the list of tools for motif discovery:
 

Perform secondary structure predictions on protein sequences.

Find binding specificity information about DNA-protein complexes.

Find information about the binding specificity of DNA-binding proteins.

Use this web-based sequence motif visualization system to display sequence motif information in its appropriate three-dimensional (3D) context.

Protein function prediction and annotation in an integrated environment powered by web service.

Use to predict function from de novo protein sequences.

Search for short active protein sequences with demonstrated biological activities.

Search for ungapped segments corresponding to the most highly conserved regions of proteins.

Identify and measure surface accessible pockets as well as interior inaccessible cavities, for proteins and other molecules.

To search for catalytic residue annotation for enzymes in the Protein Data Bank.

Predict protein function using Gene Ontology.

Automatically calculate evolutionary conservation scores of key amino acid residues and map them on protein structures.

Mine the protein structure space.

Predict short linear motifs (3-8 residues) in a set of protein sequences.

A web client for visualizing protein sequence feature information using DAS.

Identify the domain architecture within a protein sequence.

Predict functional sites in eukaryotic proteins.

Use a collection of tools for protein analyses.

Predict potential protein post-translational modifications and find potential single amino acid substitutions in peptides.

Search for information related to the catalytic mechanisms of enzymes.

An integrated feature-based function prediction server for vertebrate proteomes.

Identify the closest matching PRINTS sequence motif fingerprints in a protein sequence.

Search for functional annotation of important sites in proteins with known structures.

Produce 3D conformations of small drug compounds.

A database presenting experiment-based results in human proteomics.

Conduct exhaustive intermediate profile searches of a set of homologous protein sequences.

Design protein mutations in site-directed mutagenesis.

Annotate protein using this integrated annotation resource.

Identify protein family (and DNA) domains, patterns, motifs, protein families, and functional sites.

Interactive forecasting of protein interaction hot spots.

Database containing pairs of structural analogs and their alignments.

Find sequence patterns in DNA and protein sequences.

A web server for knowledge-based modeling of protein-peptide complexes, specifically peptides in complex with major histocompatibility complex (MHC) proteins and kinases.

Find structural segments or motifs for protein structures.

Visualize protein sequence motifs on the 3D protein structures.

Find presence of any known protein motif (Prosite and Pfam) in a protein sequence.

Recognize spatial chemical binding patterns common to a set of protein structures.

Analyze proteins for the presence of N-terminal N-myristoylation site.

Find the presence of N-Glycosylation sites in human proteins.

Find the presence of O-GalNAc (mucin type) glycosylation sites in mammalian proteins.

Analyze eukaryotic proteins for the presence of serine, threonine and tyrosine phosphorylation sites.

Find possible kinase specific phosphorylation sites in eukaryotic proteins.

Predict cleavage sites at basic amino acid locations in neuropeptide precursor sequences.

Find information about patented nucleotide and protein sequences.

Search for information about glycoproteins with O-linked and C-linked glycosylation sites.

Find information about protein sequence annotations.

A server to predict protein active site residues.

Search for structural and functional information on the protein functional sites.

Search 3D protein fragments similar in structure to known active, binding and posttranslational modification sites.

Conduct genome wide functional and structural analysis.

Search for phosphorylation data of any protein of interest.

Search for information on prokaryotic proteins that undergo serine, threonine, or tyrosine phosphorylation.

Determine correct names for proteins.

Web application for predicting protein disorder by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.

Find homologous protein-protein interactions across multiple species.

Search your query sequence against PROSITE pattern database for protein motifs.

Find information about protein-RNA complexes from the Protein Data Bank (PDB).

Identify protein families and domains for a given protein sequence.

A comprehensive database of pattern-recognition receptors and their ligands.

Search for short nucleotide or peptide sequences such as cis-elements in nucleotide sequences or small domains and motifs in protein sequences.

Database specialized in documenting human PPBD-containing proteins and PPBD-mediated interactions.

Predicts potential protease cleavage sites and sites cleaved by chemicals in a given protein sequence.

Predict combined transmembrane topology and signal peptides.

Search for eukaryotic phosphorylation sites.

Search for 3D structure and functional annotation of phosphorylation sites in proteins.

Search the database of in vivo phosphorylation sites of human and mouse proteins

Find information about polyglutamine (polyQ) repeats.

Find the presence of protein motifs and patterns in an amino acid sequence.

Predict signal peptide sequences and their cleavage positions in bacterial and eukaryotic amino acid sequences.

Predict protein functions based on known structures.

Predict the location of potential protein-protein binding sites for unbound proteins.

Identify short linear signatures in protein termini.

Analyze and identify newly obtained protein sequences.

Predict protein binding sites in a protein sequence based on geometrical analysis of protein tertiary substructures.

Search for evolutionarily conserved motif-like patterns in protein sequences.

Web-based server for analyzing and predicting RNA binding sites in proteins.

Search for similarities between proteins by simultaneous matching of multiple motifs.

Predict residues in protein sequences that determine the proteins' functional specificity.

Predict specificity-determining residues in protein families.

Find shared motifs in proteins with a common attribute.

Conduct in silico sumoylation sites prediction.

Search for motifs and patterns within protein sequences.

Search for motifs within proteins that are likely to be phosphorylated by specific protein kinases or bind to domains such as SH2 domains, 14-3-3 domains or PDZ domains.

Find information about populations carrying polymorphisms within protein binding pockets that make them susceptible to serious adverse drug reaction (SADR).

Search the presence of a motif in either amino acid sequence or nucleotide sequence.

Predict the presence of tyrosine sulfation sites in protein sequences

Look at protein structure from a ligand and binding site perspective.

Use a collection of bioinformatics tools at this portal site.

Find information about tandem repeats in proteins that carry fundamental biological functions and are related to a number of human diseases.

Find information about functional residues in alpha-helical and beta-barrel membrane proteins.

Database of domains and motifs with conservative location in transmembrane proteins.

Search for highly conserved and specific protein sequence motifs.

Predict functional sites in protein sequence alignments use different methodologies.

Find de novo protein motifs from chromatin immunoprecipitation data.

Scan query structures for functional sites in both proteins and nucleic acids.

Analyze quantitative structure-activity relationship of related protein families.

Search for ungapped alignments of highly conserved regions among a protein family or superfamily.

An expert system for predicting ligand-binding residues in protein structures.

Automatically identify spatially interacting motifs among distantly related proteins sharing similar folds and possessing common ancestral lineage.

]]> Neel https://bioinformaticsonline.com/news/view/396/bioinformatics-introduction-to-perl Thu, 11 Jul 2013 09:49:37 -0500 https://bioinformaticsonline.com/news/view/396/bioinformatics-introduction-to-perl <![CDATA[Bioinformatics: Introduction to PERL]]> This course is aimed at those new to programming and provides an introduction to programming using Perl. By the end of this course, attendees should be able to write simple Perl programs and to understand more complex Perl programs written by others. The course will be taught using the online Learning Perl materials created by Sofia Robb of the University of California Riverside. Further information is available.

]]> Archana Malhotra