BOL: Related items

LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets

LEGE — Fri, 17 Sep 2021 01:57:02 -0500

The pipeline was developed based on a popular workflow framework Nextflow, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report. LncPipe allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.

Ref https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/

Address of the bookmark: https://github.com/likelet/LncPipe

proActiv: Estimation of Promoter Activity from RNA-Seq data

BioStar — Thu, 13 Aug 2020 10:21:44 -0500

proActiv is an R package that estimates promoter activity from RNA-Seq data. proActiv uses aligned reads and genome annotations as input, and provides absolute and relative promoter activity as output. The package can be used to identify active promoters and alternative promoters, the details of the method are described in Demircioglu et al.

Additional data on differential promoters in tissues and cancers from TCGA, ICGC, GTEx, and PCAWG can be downloaded here: https://jglab.org/data-and-software/

Address of the bookmark: https://github.com/GoekeLab/proActiv

Exploring RNA Sequence Analysis: Tools for Every Bioinformatician

Neel — Fri, 13 Dec 2024 04:03:04 -0600

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.

Bioinformatics approach to Boar Taint

Rahul Agarwal — Wed, 17 Jul 2013 15:50:37 -0500

Meat products obtained from intact male pigs often produce offensive smell or odour which is recognized as a complex genetic trait called boar taint.Androstenone and Skatole in the fat primarily cause boar taint. Metabolism of androstenone and sex steroids share a common pathway which makes removal of boar taint a very challenging task. Castration is a traditional solution to remove boar taint but it also results in bad quality of meat due to low level of steroids which is objectionable to many consumers. Detected functional variant(s) underlying boar taint compounds can be used as genetic markers in selection of male pigs with reduced boar taint levels. Resequencing of a total of 47 samples belong to Norwegian Landrace (NL) and Duroc (D) pigs with varied boar taint levels were done in Illumina HiSeq2000 to >10X average coverage. Short reads generated from these samples mapped to Sus Scrofa version 10.2 reference assembly using Bowtie2. Alignment file then used for calling SNPs and InDels inside previousy identified QTL regions on SSC5,13, and 7 with the aid of FreeBayes , a variant caller tool. A final list of SNPs was prepared after filtering SNPs on the basis of SNP quality, coverage of SNP allele, functional and structural annotation, and repeats, etc. Selected SNPs will be genotyped in sample population for validation and then used for constructing SNPs haplotypes in close linkage disequilibrium with QTLs and fine mapping of QTLs through association mapping of genotyped SNPs.

Genome Browsers

Rahul Agarwal — Fri, 16 Aug 2013 19:04:47 -0500

Genome Browser is the platform/database used for searching and retreiving sequences and annotation of genomes belong to various eukaryotes, prokaryotes, etc.

Following are the weblink for different available browsers:

http://www.ensembl.org/index.html

http://ensemblgenomes.org/

http://genome.ucsc.edu/

http://www.ncbi.nlm.nih.gov/genome

http://www.ebi.ac.uk/genomes/

http://flybase.org/

http://cmr.jcvi.org/tigr-scripts/CMR/CmrHomePage.cgi

http://www.sanger.ac.uk/resources/databases/

Two major breakthrough!!

Rahul Agarwal — Mon, 02 Sep 2013 10:18:11 -0500

"Scientists in Uruguay in colloboration with European partners sequenced the genome of the high-value Tannat grape, from which "the most healthy of red wines" are fermented.

A quick, $1 syphilis test in development by researchers from UNU-BIOLAC."

Source:

http://www.sciencedaily.com/releases/2013/09/130902101846.htm

http://www.eurekalert.org/pub_releases/2013-09/tca-ssg082613.php

Largest Genome Sequenced

Rahul Agarwal — Fri, 21 Mar 2014 13:57:19 -0500

The enormous size of the loblolly pine genome having 22 billion base pairs compared to only 3 billion in the human genome. In other words, it is seven times larger than a human’s and also the largest and the most complete conifer genome ever sequenced.

Related Paper:

http://genomebiology.com/2014/15/3/R59/abstract

Address of the bookmark: http://www.news.ucdavis.edu/search/news_detail.lasso?id=10859

Deadly Human Pathogen Cryptococcus Sequenced

Rahul Agarwal — Fri, 25 Apr 2014 11:02:21 -0500

"Now, researchers have sequenced the entire genome and all the RNA products of the most important pathogenic lineage of Cryptococcus neoformans, a strain called H99. The results, which appear in PLOS Genetics, also describe a number of genetic changes that can occur after laboratory handling of H99 that make it more susceptible to stress, hamper its ability to sexually reproduce and render it less virulent."

Source:

http://www.biosciencetechnology.com/news/2014/04/deadly-human-pathogen-cryptococcus-fully-sequenced

Paper:

http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1004292

miRNA database and tools

Rahul Agarwal — Mon, 09 Jun 2014 07:58:40 -0500

Since few years miRNA has shown to play important role in therapeutic related research and also known to play vital role in controlling gene expression specifically at transcriptional and post-transcription levels. Here are some important DBs and tools related with miRNA:

miRNA Sequencing data analysis : http://tools.genxpro.net/omiras/

miRNApath( R based tool) : http://www.bioconductor.org/packages/release/bioc/html/miRNApath.html

miRWalk DB : http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/

TargetScanHuman : http://www.targetscan.org/

RNAhybrid : http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/welcome.html

RNA22 predictor : http://cbcsrv.watson.ibm.com/rna22.html

miRNA predictor : http://www.microrna.org/microrna/home.do

Plant miRNA DB :http://bioinformatics.cau.edu.cn/PMRD/

miRBASE DB: http://www.mirbase.org/

Plant RNA predictor : http://plantgrn.noble.org/psRNATarget/

miRNA Interaction DB : http://starbase.sysu.edu.cn/

Sequencing based miRNA DB : http://mirgator.kobic.re.kr/

predicted A-to-I edited miRNA DB : http://microrna.osumc.edu/mireditar/

Animal, plant and virus miRNA DB : http://lemur.amu.edu.pl/share/php/mirnest/

Atlantic Salmon miRNAs DB : http://www.molgenv.com/ssa_mirnas_db_home.php

miRNA prediction on UTRs : http://genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html

Idea of analysing miRNA Sequencing data :

http://www.illumina.com/applications/epigenetics/small_rna_analysis.ilmn

More:

http://www.bioconductor.org/help/search/index.html?q=miRNA+target

GraphMap - A highly sensitive and accurate mapper for long, error-prone reads

Rahul Agarwal — Mon, 06 Jul 2015 08:46:53 -0500

GraphMap is a novel mapper targeted at aligning long, error-prone third-generation sequencing data.
It is designed to handle Oxford Nanopore MinION 1d and 2d reads with very high sensitivity and accuracy, and also presents a significant improvement over the state-of-the-art for PacBio read mappers.

GraphMap was also designed for ease-of-use: the default parameters can handle a wide range of read lengths and error profiles, including: Illumina, PacBio and Oxford Nanopore.
This is an especially important feature for technologies where the error rates and error profiles can vary widely across, or even within, sequencing runs.

http://biorxiv.org/content/early/2015/06/10/020719

Address of the bookmark: https://github.com/isovic/graphmap