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.

SiLiX adopts a graph-theoretical framework to interpret similarity pairs as edges of a network. A very efficient algorithm, based on the Disjoint Sets Data Structure, allows the computation of sequence families with low time and space requirements.

A parallel version of SiLiX, based on MPI, is also available in this package and has been proved to be scalable, so that its allows the study of very large datasets.

SiLiX is already included in the analysis pipeline for HOGENOM.

Address of the bookmark: http://lbbe.univ-lyon1.fr/SiLiX?lang=fr

It is enormous, billions of letters long. Some of those letters are easy to understand because they contain instructions for building proteins, the tiny machines that keep our cells alive.

But what if some of the most important instructions aren't in those obvious chapters?

Scientists have long suspected that the mysterious stretches of DNA between protein-coding genes may contain some of life's most interesting secrets. These regions can act like switches, telling genes when, where, and how strongly to turn on. Among them are conserved non-coding elements, or CNEs DNA sequences that have remained remarkably similar across different species.

Think of a CNE as a sentence that nature has copied into several editions of the same book.

A mouse has it.

A bird has it.

A human has it.

And despite millions of years of evolution, much of the sentence remains almost unchanged.

That conservation is a clue: this piece of DNA probably matters.

But there was a problem. Finding these hidden sentences across enormous genomes is difficult. Researchers needed to compare many genomes, identify conserved regions, and then determine which of those regions might have evolved unusually quickly in particular branches of the evolutionary tree.

Enter CNEwrap. https://github.com/YanCCscu/CNEwrap

The researchers developed CNEwrap as a streamlined toolkit capable of handling large-scale genome comparisons, conserved-element detection, and evolutionary analysis. Inside it is a new algorithm called EvoAcc, designed to identify CNEs that show accelerated evolution in specific species or lineages.

Why does that matter?

Because evolution isn't just about what stays the same.

Sometimes, the most interesting story is hidden in what changed.

Imagine that nearly every species carries the same regulatory sentence, but one lineage suddenly has several unusual edits. Perhaps those changes helped shape a new body structure, a different adaptation, or another distinctive trait.

EvoAcc is designed to help researchers find these evolutionary clues. In the study's tests, it performed particularly well in scenarios involving two or three accelerated lineages and showed improved sensitivity to insertion and deletion mutations compared with several existing approaches.

The researchers also tested CNEwrap using functional genomic regions from mammals and found that EvoAcc could recover human-specific accelerated regions while identifying some signals that other methods missed.

So the story of CNEwrap is really a story about looking beyond the obvious.

For decades, scientists have often focused on the DNA that directly encodes proteins. But the genome is more like a vast library: the protein-coding genes are the main text, while non-coding regions may contain punctuation, instructions, switches, annotations, and editing notes that tell the cell how to read the book.

CNEwrap gives researchers a new way to search that hidden material.

And perhaps somewhere inside those billions of DNA letters is an evolutionary story we haven't read yet.

The exciting part is that we now have better tools to find it.

Read more about it https://academic.oup.com/nar/article/54/14/gkag709/8738333?

AutoDock

Stochastic (GA)

Flexible ligand and partially flexible target

ArgusLab

Systematic

Flexible ligandX-Score based

DOCK

Systematic (IC)

Flexible ligandDOCK 3.5 (force field)

eHITS

Systematic (RBD of fragments followed by reconstruction)Flexible ligand and partially flexible targetHiTS_Score (empirical)

FlexX

Systematic (IC)Flexible ligandFlexX SF (empirical)Commercial

FLIPDock

Stochastic (GA)Flexible ligand and flexible targetAUTODOCK (empirical)

FRED

Systematic (RBD)Flexible ligandChemScore, PLP, ScreenScore, ChemGauss (empirical/consensus)

GOLD

Stochastic (GA)

Flexible ligand and partially flexible targetGoldScore, ChemScore (empirical), ASP (knowledge based)

ICM

Stochastic (MC)

Flexible ligand and partially flexible targetICM SF (empirical)

ParDOCK

Stochastic (MC)

RigidBAPPL (empirical)

PLANTS

Stochastic (ACO)Flexible ligand and partially flexible target

CHEMPLP, PLP (empirical)

Surflex

Systematic (IC/MA)Flexible ligandHammerhead based (empirical)

Point to note:

Several studies have shown that the performance of most docking tools is highly dependent on the particular characteristics of both the binding site and the ligand to be investigated, and the determination which method would be more suitable in a specific context is difficult. We encouraged you to check several docking methods to determine which one(s) work best for your system.

Address of the bookmark: https://github.com/rrwick/Unicycler

Detail paper at https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-3927-8

Address of the bookmark: https://github.com/VicugnaPacos/ALPACA

---how genetic variation is distributed within and between individuals,
---determining how this diversity changes over evolutionary time.

Hence, Cox group work at the interface between biology, statistics and computer science to address questions of outstanding biological importance through intrepretation of large genetic datasets.

Profile:
Associate Professor Murray Cox,
Inaugural Rutherford Fellow of the Royal Society of New Zealand, Principal Investigator in the BioProtection Research Center and Associate Investigator in the Allan Wilson Center for Molecular Ecology and Evolution
Email : m.p.cox@massey.ac.nz
Webpage: http://massey.genomicus.com/index.html

https://bio7.org/about/

Address of the bookmark: https://bio7.org/home-2/