https://liulab-dfci.github.io/software/

“The useful features of NgAgo for genome editing include the following.First, it has a low tolerance to guide–target mismatch. A single nucleotide mismatch at each position of the gDNA impaired the cleavage efficiency of NgAgo, and mismatches at three positions completely blocked cleavage in our experiments. Second, 5′ phosphorylated short ssDNAs are rare in mammalian cells, which minimizes the possibility of cellular oligonucleotides misguiding NgAgo.Third, NgAgo follows a ‘one-guide-faithful’ rule, that is, a guide can only be loaded when NgAgo protein is in the process of expression, and, once loaded, NgAgo cannot swap its gDNA with other free ssDNA at 37 °C. All of these features could minimize off-target effects. Finally, it is easy to design and synthesize ssDNAs and to adjust their concentration, which is difficult with the Cas9-sgRNA system, if the sgRNA is expressed from a plasmid and the normal dosage of an ssDNA guide is only ~1/10 of that of a sgRNA expression plasmid.

NgAgo might be a more orderly way and perhaps even simpler way to go about genome editing than CRISPR, but the jury is still out on that until there are more papers and data. The NgAgo edit efficiency at this preliminary stage of technology development seems very strong. See the pics below

Reference: http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.3547.html

To address the challenges of describing and estimating virodiversity, a team of investigators from Center for Infection and Immunity (CII) and EcoHealth Alliance began in jungles of Bangladesh - home to the flying fox.

Reference:

http://economictimes.indiatimes.com/news/news-by-industry/et-cetera/mammals-harbour-at-least-320000-new-viruses/articleshow/22253268.cms

http://www.bbc.co.uk/news/science-environment-23932400

Lab page @ http://graveleylab.cam.uchc.edu/Graveley/index.html

The image summarise all in one.

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

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

https://salzberg-lab.org/

In this project, you will use single-molecule techniques to label mRNA and DNA in (live and fixed) Drosophila embryos and fixed embryonic bodies. You will also use super-resolution microscopy to visualize protein condensates. Co-localization dynamics reflecting DNA-protein bindings and DNA looping events will be detected, analyzed, and used to test computational models of gene transcription.

Qualification:
MSc degree (or equivalent) in Biology, Biophysics, or Bioengineering

Experience in one or more of these areas: (1) molecular cloning, (2) imaging, (3) image analysis (using Matlab/Python/Java), (4) microfluidics, and (5) computational modeling.

How to Apply?
Send (1) your CV, (2) summary of research experience, and (3) email addresses of at least 2 references to ezzat.el-sherif@fau.de. Title your email ‘Transcription PhD Position’.

salary Grade.: E13
Total Time: 3 Jahre
Start: 01.01.2020.
End: 31.3.2020.

Address:
Dr. El-Sherif, Ezzat
Department Biologie
Professur für Zoologie (Entwicklungsbiologie) (Prof. Dr. Klingler)
Telefon 09131/85-28068, Fax 09131/85-28040, E-Mail: ezzat.el-sherif@fau.de

Protein-protein and protein-DNA/RNA docking based on a hybrid algorithm of template-based modeling and ab initio free docking.

The HDOCK server distinguishes itself from similar docking servers in its ability to support amino acid sequences as input and a hybrid docking strategy in which experimental information about the protein–protein binding site and small-angle X-ray scattering can be incorporated during the docking and post-docking processes.

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

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.