BOL: Related items

RNA Sequencing Helps Identify Functional Variants from GWAS

Andaleeb — Fri, 22 Nov 2013 21:33:33 -0600

For Alzheimer’s and other complex disorders, mining the genome for disease-associated variants is no longer the obstacle. The challenge nowadays is figuring out how the identified loci relate to disease. As reported last month in Nature and its associated journals, advances in high-throughput RNA sequencing are providing new tools for understanding how disease loci influence gene expression—a starting point for understanding their connection to pathogenesis.

Address of the bookmark: http://schizophreniaforum.org/new/detail.asp?id=1953

Hoffman Lab

Tue, 12 Jan 2016 02:47:41 -0600

They develop machine learning techniques to better understand chromatin biology. These models and algorithms transform high-dimensional functional genomics data into interpretable patterns and lead to new biological insight.

https://www.pmgenomics.ca/hoffmanlab/

Barrnap: Bacterial ribosomal RNA predictor

Abhimanyu Singh — Fri, 12 May 2017 09:24:41 -0500

Barrnap predicts the location of ribosomal RNA genes in genomes. It supports bacteria (5S,23S,16S), archaea (5S,5.8S,23S,16S), mitochondria (12S,16S) and eukaryotes (5S,5.8S,28S,18S).

It takes FASTA DNA sequence as input, and write GFF3 as output. It uses the new NHMMER tool that comes with HMMER 3.1 for HMM searching in RNA:DNA style. NHMMER binaries for 64-bit Linux and Mac OS X are included and will be auto-detected. Multithreading is supported and one can expect roughly linear speed-ups with more CPUs.

Address of the bookmark: https://github.com/tseemann/barrnap

HNADOCK: a nucleic acid docking server for modeling RNA/DNA–RNA/DNA 3D complex structures

Poonam Mahapatra — Thu, 04 Jun 2020 23:19:07 -0500

The HNADOCK server is to predict the binding complex structure between two nucleic acid molecules through a hierarchical docking algorihtm of an FFT-based global search strategy and an intrinsic scoring function for nucleic acid interactions. Users are required to provide the three-dimensional (3D) structures of the two molecules to be docked.

Address of the bookmark: http://huanglab.phys.hust.edu.cn/hnadock/

Tools for RNA classification

Abhi — Tue, 08 Nov 2022 03:39:11 -0600

barrnap - https://github.com/tseemann/barrnap

CPAT - https://github.com/liguowang/cpat, http://lilab.research.bcm.edu/ (web server)

CPC2 - https://github.com/gao-lab/CPC2_standalone, http://cpc2.gao-lab.org/ (web server)

Infernal - http://eddylab.org/infernal/, https://github.com/EddyRivasLab/infernal

NCBI RefSeq - https://www.ncbi.nlm.nih.gov/refseq/

Rfam - http://rfam.xfam.org/, https://docs.rfam.org/en/latest/index.html

SILVA - https://www.arb-silva.de/

RNAmmer - http://www.cbs.dtu.dk/services/RNAmmer/ (web server, standalone download link)

Understanding RNA-Seq Normalization Methods: TPM vs. FPKM vs. CPM

Neel — Wed, 11 Dec 2024 00:59:15 -0600

RNA sequencing (RNA-Seq) is a powerful technology used to study transcriptomes, providing insights into gene expression levels. However, raw RNA-Seq data requires normalization to account for sequencing depth and gene length, enabling accurate comparisons between genes and samples. Among the most widely used normalization methods are TPM (Transcripts Per Million), FPKM (Fragments Per Kilobase Million), and CPM (Counts Per Million). Each method has its unique principles and applications, which we’ll explore in this blog.

Why Normalize RNA-Seq Data?

Normalization is a crucial step in RNA-Seq analysis for the following reasons:

Sequencing depth: Different RNA-Seq experiments produce varying numbers of reads, making direct comparisons between samples misleading.
Gene length: Longer genes inherently generate more reads, irrespective of their actual expression level.
Bias reduction: Normalization mitigates technical biases, enabling meaningful biological interpretation.

TPM (Transcripts Per Million)

TPM measures the proportion of reads mapped to a transcript, normalized by transcript length and sequencing depth. It is calculated as:

Key Features:

Proportionality: TPM values sum to 1,000,000 across all transcripts in a sample, making it easier to compare between samples.
Intuitive interpretation: TPM values directly represent the abundance of transcripts in a sample.
Preferred for comparisons: TPM facilitates between-sample comparisons better than FPKM.

FPKM (Fragments Per Kilobase Million)

FPKM normalizes read counts by transcript length and sequencing depth, but without enforcing proportionality like TPM. It is defined as:

Key Features:

Historical significance: FPKM was one of the first normalization methods used for RNA-Seq.
Single-end vs. paired-end: In paired-end sequencing, FPKM becomes RPKM (Reads Per Kilobase Million).
Limited utility: FPKM values are not as robust as TPM for cross-sample comparisons due to lack of proportionality.

CPM (Counts Per Million)

CPM normalizes raw read counts by sequencing depth, without considering gene length. It is expressed as:

Key Features:

Simplicity: CPM is straightforward and computationally less intensive.
Application: Suitable for non-length-dependent analyses, such as comparing total expression levels or differential expression analysis.
Gene length agnostic: CPM does not correct for gene length, making it less ideal for measuring expression levels.

When to Use Each Method

TPM: Best for comparing expression levels between samples, especially when transcript length and sequencing depth vary.
FPKM: Useful for historical consistency but generally replaced by TPM.
CPM: Ideal for differential expression analysis when gene length normalization is unnecessary.

Conclusion

Choosing the right normalization method depends on the specific objectives of your RNA-Seq analysis. TPM’s proportionality and robustness make it the preferred choice for most applications, while CPM serves well for differential expression studies. Although FPKM paved the way for RNA-Seq normalization, it has largely been supplanted by TPM in modern workflows. Understanding these methods and their nuances ensures accurate and meaningful interpretations of RNA-Seq data.

References:

Li, B., & Dewey, C. N. (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics.
Trapnell, C., et al. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology.
Law, C. W., et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology.

Coronavirus Resources !

Neel — Wed, 25 Mar 2020 17:11:33 -0500

2019nCoVR features comprehensive integration of genomic and proteomic sequences as well as their metadata information from the GISAID, NCBI, NMDC and CNCB/NGDC. It also incorporates a wide range of relevant information including scientific literatures, news, and popular articles for science dissemination, and provides visualization functionalities for genome variation analysis results based on all collected 2019-nCoV strains.

Annotation

https://bigd.big.ac.cn/ncov/variation/annotation

Genome wharehouse

https://bigd.big.ac.cn/gwh/browse/index

Released Genome

https://bigd.big.ac.cn/ncov/release_genome

Download data

ftp://download.big.ac.cn/Genome/Viruses/Coronaviridae/

Raw data

https://bigd.big.ac.cn/gsa/browse/run/?tag=Coronaviridae

Address of the bookmark: https://bigd.big.ac.cn/ncov/about

Sequencing Solutions to World Health

Rahul Agarwal — Thu, 29 Aug 2013 15:05:35 -0500

"New technology that quickly, easily and economically reveals the genomes of viruses and pathogens transforms public health and medicine."

Source: Life technologies

Address of the bookmark: http://www.lifetechnologies.com/global/en/home/communities-social/blog/blogs/sequencing-solutions-to-world-health.html?cid=social_blogseries_20130829_11098264

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/