BOL: Related items

Genetic code - Amino Acid

Poonam Mahapatra — Sun, 26 Oct 2014 07:45:58 -0500

The genetic code consists of 64 triplets of nucleotides. These triplets are called codons.With three exceptions, each codon encodes for one of the 20 amino acids used in the synthesis of proteins. That produces some redundancy in the code: most of the amino acids being encoded by more than one codon.

The image summarise all in one.

More at http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/C/Codons.html

R-chie

Jit — Thu, 01 Sep 2016 11:47:24 -0500

R-chie allows you to make arc diagrams of RNA secondary structures, allowing for easy comparison and overlap of two structures, rank and display basepairs in colour and to also visualize corresponding multiple sequence alignments and co-variation information.
R4RNA is the R package powering R-chie, available for download and local use for more customized figures and scripting.

http://www.e-rna.org/r-chie/plot.cgi?eg=single

Address of the bookmark: http://www.e-rna.org/r-chie/plot.cgi?eg=single

Salzberg lab

Mon, 15 May 2017 05:14:01 -0500

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/

iRNAD: a computational tool for identifying D modification sites in RNA sequence

Abhimanyu Singh — Thu, 16 May 2019 00:20:07 -0500

iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification.

http://lin-group.cn/server/iRNAD/

Address of the bookmark: http://lin-group.cn/server/iRNAD/

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)

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.

DNA RNA MEME

Neel — Thu, 28 Jan 2021 11:23:14 -0600

Explain the DNA and RNA with picture ...

DIY Transcriptomics

Abhi — Wed, 31 Jul 2024 01:19:26 -0500

A semester-long course covering best practices for the analysis of high-throughput sequencing data from gene expression (RNA-seq) studies, with a primary focus on empowering students to be independent in the use of lightweight and open-source software using the R programming language and the Bioconductor suite of packages. This course follows a hybrid format in which online lectures are paired with in-person labs where students participate in hands-on, live coding exercises using real ‘omic datasets. The course is focused on datasets and topics central to infectious disease research, immunology, and One-Health, but the concepts and approaches covered are applicable to any genomic study.

https://diytranscriptomics.com

Address of the bookmark: https://diytranscriptomics.com

Introduction to Bioinformatics

eliabrodsky — Wed, 05 Jun 2019 14:58:11 -0500

Introduction to bioinformatics is a course for biologists and clinicians that would like to learn more about the way bioinformatics is used in healthcare, biotech and pharmaceuitcal industry as well as basic research. The course covers many of the topics transformed by the emergence of big data and computational technologies. To learn more about the course, visit: https://edu.t-bio.info/course/introduction-bioinformatics/