<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/42974?offset=10 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/blog/view/44716/exploring-rna-sequence-analysis-tools-for-every-bioinformatician Fri, 13 Dec 2024 04:03:04 -0600 https://bioinformaticsonline.com/blog/view/44716/exploring-rna-sequence-analysis-tools-for-every-bioinformatician <![CDATA[Exploring RNA Sequence Analysis: Tools for Every Bioinformatician]]> RNA sequence analysis has become an essential part of modern biological research. From RNA-seq pipelines to specialized tools for specific RNA types, here's a comprehensive guide to tools you can use to make sense of RNA data.

1. RNA-Seq Analysis Pipelines

RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:

  • FASTQC: For quality control of raw RNA-seq reads.
  • Trimmomatic: For trimming and filtering RNA-seq reads.
  • HISAT2/STAR: High-performance aligners for RNA-seq reads.
  • FeatureCounts: For quantifying gene expression.
  • DESeq2/EdgeR: For differential expression analysis.

2. Transcriptome Assembly and Annotation

For analyzing transcriptomes from non-model organisms or assembling novel transcripts:

  • Trinity: For de novo transcriptome assembly.
  • StringTie: For transcript assembly and quantification from RNA-seq alignments.
  • TransDecoder: To predict coding regions within assembled transcripts.
  • TAU: Tools for annotating non-coding and coding RNAs.

3. Exploring Non-Coding RNA (ncRNA)

Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:

  • Infernal: For identifying ncRNA sequences based on covariance models.
  • Rfam: Database and tools for ncRNA families.
  • miRDeep: For identifying microRNAs in RNA-seq datasets.

4. RNA Structure and Motif Analysis

Structural biology of RNA helps in understanding its function:

  • RNAfold (ViennaRNA): Predicts secondary structures from RNA sequences.
  • RNAstructure: Tools for RNA secondary structure prediction and analysis.
  • MEME Suite: For identifying motifs in RNA sequences.
  • IntaRNA: For RNA-RNA interaction prediction.

5. RNA Editing and Modifications

Epitranscriptomics is a growing field focusing on RNA modifications:

  • REDItools: For RNA editing analysis.
  • m6Aboost: For identifying m6A modifications in RNA.

6. Long-Read RNA Sequencing Analysis

Long-read technologies like Nanopore and PacBio are transforming RNA research:

  • FLAIR: For isoform-level analysis of long-read RNA-seq data.
  • NanoMod: For detecting modifications in RNA from Nanopore sequencing.

7. RNA-Protein Interactions

To study RNA-protein interactions and complexes:

  • RBPmap: For identifying RNA-binding protein motifs.
  • PARalyzer: For analyzing PAR-CLIP data.

8. Functional Enrichment Analysis

Understanding biological functions and pathways from RNA-seq data:

  • getENRICH: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).
  • ClusterProfiler: For GO and KEGG pathway enrichment analysis.

9. Visualization and Data Sharing

Presenting and sharing RNA sequence analysis results effectively:

  • IGV: Genome browser for visualizing RNA-seq alignments.
  • Circos: Circular visualization of RNA-seq data.
  • DashBio: A Python library for creating bioinformatics visualizations.

Conclusion

The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you’re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.

]]> Neel https://bioinformaticsonline.com/researchlabs/view/40882/troyanskaya-lab Tue, 04 Feb 2020 06:40:36 -0600 <![CDATA[Troyanskaya Lab]]> The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets. We apply these approaches in studying challenging biological problems, such as how pathways function in diverse cell types and how they change dynamically.

https://function.princeton.edu/

]]> https://bioinformaticsonline.com/bookmarks/view/17924/software-developed-in-pevsner-lab Mon, 06 Oct 2014 12:41:26 -0500 https://bioinformaticsonline.com/bookmarks/view/17924/software-developed-in-pevsner-lab <![CDATA[Software developed in pevsner lab]]>

DRAGON: Database Referencing of Array Genes Online

SNOMAD: Standardization and Normalization of Microarray Data

SNPduo: SNP Analysis Between Two Individuals

SNPtrio: Analyzing and Visualizing and Inheritance Patterns in Trios

SNPscan: Data Analysis and Visualization of SNP Data

pediSNP: Analyze SNP Data From a Pedigree of Two Generations

kcoeff: Calculate Cotterman Coefficients of SNP Genotype Data

triPOD: Detects chromosomal abnormalities in parent-child trio-based microarray data

Address of the bookmark: http://pevsnerlab.kennedykrieger.org/php/?q=software

]]> Robert M Willioms https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines Sat, 21 May 2016 22:42:16 -0500 https://bioinformaticsonline.com/bookmarks/view/27463/bpipe-a-tool-for-running-and-managing-bioinformatics-pipelines <![CDATA[Bpipe - a tool for running and managing bioinformatics pipelines]]> Bpipe provides a platform for running big bioinformatics jobs that consist of a series of processing stages - known as 'pipelines'.

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse Wed, 22 Jun 2016 05:37:38 -0500 https://bioinformaticsonline.com/bookmarks/view/27959/darkhorse <![CDATA[DarkHorse]]> DarkHorse is a bioinformatic method for rapid, automated identification and ranking of phylogenetically atypical proteins on a genome-wide basis. It works by selecting potential ortholog matches from a reference database of amino acid sequences, then using these matches to calculate a lineage probability index (LPI) score for each genome protein.

LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scalede novo predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.

One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.

DarkHorse is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the references cited below for a complete explanation of parameter selection and result interpretation. A brief tutorial page is also available on-line.

Address of the bookmark: http://darkhorse.ucsd.edu/download.html

]]> Jit https://bioinformaticsonline.com/bookmarks/view/28915/useful-bioinformatics-tools Mon, 29 Aug 2016 04:08:12 -0500 https://bioinformaticsonline.com/bookmarks/view/28915/useful-bioinformatics-tools <![CDATA[Useful Bioinformatics Tools]]> Collections of few handy tools for bioinformatician

http://molbiol-tools.ca/Convert.htm

Address of the bookmark: http://molbiol-tools.ca/Convert.htm

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/30355/meme-suite Fri, 23 Dec 2016 08:49:55 -0600 https://bioinformaticsonline.com/bookmarks/view/30355/meme-suite <![CDATA[MEME suite]]> Motif based sequence analysis suits 

The MEME Suite allows the biologist to discover novel motifs in collections of unaligned nucleotide or protein sequences, and to perform a wide variety of other motif-based analyses.

The MEME Suite supports motif-based analysis of DNA, RNA and protein sequences. It provides motif discovery algorithms using both probabilistic (MEME) and discrete models (MEME), which have complementary strengths. It also allows discovery of motifs with arbitrary insertions and deletions (GLAM2). In addition to motif discovery, the MEME Suite provides tools for scanning sequences for matches to motifs (FIMO, MAST and GLAM2Scan), scanning for clusters of motifs (MCAST), comparing motifs to known motifs (Tomtom), finding preferred spacings between motifs (SpaMo), predicting the biological roles of motifs (GOMo), measuring the positional enrichment of sequences for known motifs (CentriMo), and analyzing ChIP-seq and other large datasets (MEME-ChIP).

The MEME Suite is comprised of a collection of tools that work together, as shown below. Not all the tools are available as webservices, so to get the full power of the MEME Suite you will need to download and install a local copy of the software. To see what has changed recently you can peruse the release notes.

http://meme-suite.org/

Address of the bookmark: http://meme-suite.org/

]]> Bulbul https://bioinformaticsonline.com/bookmarks/view/32018/tmap-torrent-mapping-alignment-program-general-notes Sun, 02 Apr 2017 15:53:47 -0500 https://bioinformaticsonline.com/bookmarks/view/32018/tmap-torrent-mapping-alignment-program-general-notes <![CDATA[TMAP - torrent mapping alignment program General Notes]]> TMAP - torrent mapping alignment program General Notes

TMAP is a fast and accurate alignment software for short and long nucleotide sequences produced by next-generation sequencing technologies.

  • The latest TMAP is unsupported. To use a supported version, please see the TMAP version associated with a Torrent Suite release below.

  • Get the latest source code:

    git clone git://github.com/iontorrent/TMAP.git
 cd TMAP
 git submodule init
 git submodule update

https://github.com/iontorrent/TS/tree/master/Analysis/TMAP

Address of the bookmark: https://github.com/iontorrent/TS/tree/master/Analysis/TMAP

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 Mon, 10 Apr 2017 04:45:40 -0500 https://bioinformaticsonline.com/bookmarks/view/32131/wgs-celera-assembler-version-83rc2 <![CDATA[WGS Celera Assembler version 8.3rc2]]> These are release notes for Celera Assembler version 8.3rc2, which was released on May 24, 2015.

This distribution package provides a stable, tested, documented version of the software.  The distribution is usable on most Unix-like platforms, and some platforms have pre-compiled binary distributions ready for installation.

The source code package includes full source code (revision 4627), Makefiles, and scripts.  A subset of the kmer package (http://kmer.sourceforge.net/, version r1994), used by some modules of Celera Assembler, is included.  This distribution includes [http://samtools.sourceforge.net/ SAMtools], [http://www.cbcb.umd.edu/software/jellyfish/ Jellyfish 2.0], [https://github.com/pbjd/pbutgcns PBUTGCNS], [https://github.com/PacificBiosciences/pbdagcon PBDAGCON], [https://github.com/PacificBiosciences/BLASR BLASR], and parts of the [https://github.com/PacificBiosciences/FALCON/tree/v0.1.3 Falcon assembler].

Full documentation can be found online at http://wgs-assembler.sourceforge.net/.

Interesting scripts within it

urbe@urbo214b[bin] ls                                                        []
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-rwxrwxr-x 1 urbe urbe 520K Apr 10 11:41 correctPacBio
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-rwxrwxr-x 1 urbe urbe 162K Apr 10 11:40 deduplicate
-rwxrwxr-x 1 urbe urbe  37K Apr 10 11:41 demotePosMap
-rwxrwxr-x 1 urbe urbe 1,5M Apr 10 11:41 dumpCloneMiddles
-rwxrwxr-x 1 urbe urbe 124K Apr 10 11:41 dumpPBRLayoutStore
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Address of the bookmark: http://wgs-assembler.sourceforge.net/wiki/index.php/Main_Page

]]> Jit