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

Introductory Articles/ppt/sources:

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002375

http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/Pathway%20Analysis.pdf

http://gettinggeneticsdone.blogspot.de/2012/03/pathway-analysis-for-high-throughput.html

http://davetang.org/muse/tag/pathway/

https://www.biostars.org/p/42219/

http://bioinformatics.ca//files/public/Pathways_2014_Module4_v2.pdf

http://bioinformatics.ca//files/public/Pathways_2014_Module2.pdf

Impotant Database and Tools:

GeneMANIA, Cytoscape, IPA and Metacore (Commerical ), Pathway Commons, Reactome ,Panther, BioCyc, WikiPathways, Pathvisio, KEGG, NCI, Stringdb, Amigo, WebGestalt ,ConsensusPathDB ,GSEA,Blast2go

Popular R based tools:

Reactome.db, ReactomePA, ClusterProfiler, Gage, SPIA, topGO, Pathview,DOSE,GOStat

More:

http://www.bioconductor.org/help/search/index.html?q=Enrichment+analysis+

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

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

version 1.0.0 - updates several descriptions and tests. To achieve v0.9.4, one can visit https://github.com/zhyqin/SEASTAR-0.9.4 for download.

Address of the bookmark: https://github.com/Xinglab/SEASTAR

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:

1. Proportionality: TPM values sum to 1,000,000 across all transcripts in a sample, making it easier to compare between samples.

2. Intuitive interpretation: TPM values directly represent the abundance of transcripts in a sample.

3. 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:

1. Historical significance: FPKM was one of the first normalization methods used for RNA-Seq.

2. Single-end vs. paired-end: In paired-end sequencing, FPKM becomes RPKM (Reads Per Kilobase Million).

3. 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:

1. Simplicity: CPM is straightforward and computationally less intensive.

2. Application: Suitable for non-length-dependent analyses, such as comparing total expression levels or differential expression analysis.

3. 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:

1. Li, B., & Dewey, C. N. (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics.

2. Trapnell, C., et al. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology.

3. Law, C. W., et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology.

For detail : http://www.sciencemag.org/content/341/6143/281

http://www.npr.org/blogs/health/2013/07/18/203298244/worlds-biggest-virus-may-have-ancient-roots

The ongoing 2014 West Africa Ebola outbreak is considered to be the largest and longest outbreak ever recorded of Ebola, killing at least 932 people and infecting more than 1,700 till date since March in Sierra Leone, Guinea, Nigeria and Liberia.

Hence, the World Health Organisation (WHO) on 8 August, 2014 declared the killer Ebola epidemic ravaging parts of West Africa an international health emergency.

Causes

EVD is caused by infection with a virus of the family Filoviridae, genus Ebolavirus. While there are five identified sub-species of Ebolavirus, four viruses cause disease in humans. They are Bundibugyo virus (BDBV), Ebola virus (EBOV), Sudan virus (SUDV), Taï Forest virus (TAFV).

The fifth virus, Reston virus (RESTV), is not considered to be disease-causing in humans.

According to WHO, EVD first appeared in 1976 in two simultaneous outbreaks, in Nzara, Sudan, and in Yambuku, Democratic Republic of Congo. The latter was in a village situated near the Ebola River from which the disease takes its name.

How does it spread?

It is still unclear how Ebola spreads. However, it is believed that the first pateint becomes infected through contact with an infected animal's body fluids.

Human-to-human transmission can occur through direct contact with blood, organs or other body fluids of infected people or exposure to objects such as needles and syringes that have been contaminated with infected secretions.

Ebola can also be transmitted from men who have recovered from the disease through semen as it is infectious for up to 7 weeks.

Infected dead bodies can spread Ebola as they are still infectious. So mourners who have direct contact with the body of deceased person can also get the disease.

Who is most at risk?

Health-care workers who do not wear appropriate protective clothing and family members who are in close contact with infected people or deceased patients.

Signs and symptoms:

Symptoms may occur between 2 and 21 days after contracting the infection. Common signs of Ebola include:

Fever

Headache

Muscle, abdominal and joint pain

Sore throat

Weakness

Diarrhea

Vomit or cough up blood

Chest pain

Difficulty in breathing and swallowing

Rash

Hiccups

Bleeding inside and outside the body

Prevention

Currently there is no vaccine available for humans. But the infection can be controlled through the use of recommended protective measures such as:

Avoid contacting infected blood or secretions, including from those who are dead .

Using standard precautions for all patients in the healthcare setting.

Sterilizing equipment, and wearing protective clothing including masks, gloves, gowns and goggles.

Washing your hands with soaps or detergents.

Disinfecting your surroundings.

Isolate people who have Ebola symptoms.

Culling of infected animals, with close supervision of burial or incineration of carcasses.

Yet, not travelling to the areas or countries where the virus is found is the best way to avoid Ebola.

Zoonotic Henipavirus

https://pubmed.ncbi.nlm.nih.gov/35921459/

https://www.ncbi.nlm.nih.gov/nuccore/OM069646,,OM069567,OM069568,OM069569,OM069570,OM069571,OM069572,OM069573,OM069574,OM069575,OM069576,OM069577,OM069578,OM069579,OM069580,OM069581,OM069582,OM069583,OM069584,OM069585,OM069586,OM069587,OM069588,OM069589,OM069590,OM069591,OM069592,OM069593,OM069594,OM069595,OM069596,OM069597,OM069598,OM069599,OM069600,OM069601,OM069602,OM069603,OM069604,OM069605,OM069606,OM069607,OM069608,OM069609,OM069610,OM069611,OM069612,OM069613,OM069614,OM069615,OM069616,OM069617,OM069618,OM069619,OM069620,OM069621,OM069622,OM069623,OM069624,OM069625,OM069626,OM069627,OM069628,OM069629,OM069630,OM069631,OM069632,OM069633,OM069634,OM069635,OM069636,OM069637,OM069638,OM069639,OM069640,OM069641,OM069642,OM069643,OM069644,OM069645,OM069646

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