BOL: Related items

Is reference genome necessary for gene expression study in transcriptome sequencing or for variant discovery in genome sequencing?

Rahul Agarwal — Wed, 17 Jul 2013 15:25:09 -0500

Like in case of plant genomes where nature of genome is too complex and huge in size to accomplish complete de novo assembly by current sequencing technology. What would be alternate solution? Can we live in reference free world?

iSeqQC: a tool for expression-based quality control in RNA sequencing

BioStar — Sun, 16 Feb 2020 08:47:17 -0600

iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers.

http://cancerwebpa.jefferson.edu/iSeqQC/

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3399-8

Address of the bookmark: https://github.com/gkumar09/iSeqQC

Live Webinar on RNA-Seq Data Analysis on 9 Nov 2016

Strand — Wed, 19 Oct 2016 05:25:27 -0500

Live Webinar on RNA-Seq Data Analysis

Abstract: Strand NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes Transcriptome / Genome alignment, Differential expression analysis with Statistical approach and Splicing events detection. Strand NGS also supports novel discovery like identification of novel genes, exons and Novel splice junctions, alongside it can also detect gene fusion events. Further downstream analysis such as GO and pathway analysis can be performed on the set of interesting genes. The product has an option to create pipelines for time consuming jobs which automates analysis and leaves more time for end data interpretation. This webinar will give an overview of the features in the RNA-Seq data analysis workflow in Strand NGS and also highlights on parameters within each feature that can be optimized depending on datasets and analysis needs.

Speaker: Mr. Sugandan Sivamani, Senior Application Scientist, Strand Life Sciences

Date: 9th Nov, Session 1 for SAPK/ APFO: 2:30 PM IST Date: 9th Nov, Session 2 for AFO/ EMEA: 9:00 AM PST

P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads

BioStar — Fri, 07 Sep 2018 05:19:06 -0500

P_RNA_scaffolder is a novel scaffolding tool using Pair-end RNA-seq to scaffold genome fragments. The method is suitable for most genomes. The program could utilize Illumina Paired-end RNA-sequencing reads from target speciesies. Our method provides another practical alternative to existing mate-pair_based approaches or other Protein-based approaches (for instance, PEP_scaffolder ) for scaffolding genome sequences. The most important feature of this method is to improve the completeness of gene regions and long-coding gene regions (for instance, circRNA).

Address of the bookmark: http://www.fishbrowser.org/software/P_RNA_scaffolder/#

Curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism

Rahul Nayak — Sun, 23 Feb 2020 02:17:30 -0600

MCBI have a curated set of ribosomal RNA (rRNA) reference sequences (targeted loci) with verifiable organism sources and current names. This set is critical for correctly identifying and classifying prokaryotic (bacteria and archaea) and fungal samples. To provide easy access to these sequences, we recently added a separate rRNA/ITS databases section on the nucleotide BLAST page for these targeted sequences that makes it convenient to quickly identify source organisms. The new databases are:

*16S ribosomal RNA (Bacteria and Archaea)

*18S ribosomal RNA sequences (SSU) from Fungi type and reference material

*28S ribosomal RNA sequences (LSU) from Fungi type and reference material

*Internal transcribed spacer region (ITS) from Fungi type and reference material

You can also download these from the BLAST db FTP area. See the NCBI Insights post for more detail.

Useful links

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BLAST form with rRNA/ITS databases

BLAST db download

Targeted loci

If you have any questions or concerns, please contact blast-help@ncbi.nlm.nih.gov

Encode sequencing data freely available to download and use for academic means

Rahul Agarwal — Thu, 13 Mar 2014 18:18:08 -0500

In Encode, regulatory elements investigated via DNA hypersensitivity assays, assays of DNA methylation, and chromatin immunoprecipitation (ChIP) of proteins that interact with DNA, including modified histones and transcription factors, followed by sequencing (ChIP-Seq).

More information:

https://genome.ucsc.edu/ENCODE/pilot.html

Address of the bookmark: https://genome.ucsc.edu/ENCODE/

BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data

Jit — Thu, 31 May 2018 09:35:23 -0500

BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes. The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC. https://github.com/lh3/bfc

Address of the bookmark: https://github.com/lh3/bfc

R package for visualising GO enrichment

Jit — Mon, 22 Jul 2013 12:25:09 -0500

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

BioDBnet

Jit — Thu, 02 Jun 2016 11:11:47 -0500

Database to Database Conversions

db2db allows for conversions of identifiers from one database to other database identifiers or annotations. To use db2db select the input type of your data, changing the input type automatically changes the output options to the ones specific for the input selected. Then select one or more output types and add your identifiers in the ID list box. Set the remove duplicate values to 'No' if you do not want duplicates to be removed. Clicking on submit then returns a table of your inputs matched against all the outputs selected in the exact order as entered. Results can be limited to a particular taxon by entering it's Taxon ID. The performance will vary widely depending on the number of outputs and the options selected. Conversions to a single output with the default options should complete in a few seconds

Address of the bookmark: https://biodbnet-abcc.ncifcrf.gov/db/db2db.php

WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results

BioStar — Sun, 12 Apr 2020 10:02:22 -0500

WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we’ve made:
1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.
2. We have added the reference data of 9 species for users selection.
3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.

Address of the bookmark: http://wego.genomics.org.cn/