Techniques: RNA sequencing or transcriptomics......

RNA sequencing

RNA sequencing (high-throughput cDNA sequencing or RNAseq) uses the NGS technology for discovering novel RNA sequences, and quantifying all transcripts in a cell. 
Analysis of DNA-microarray data (LimmeR)
In contrast to microarrays, RNAseq offers a broader dynamic range, which makes this platform more sensitive in the detection of transcripts with low abundance. So, RNA sequencing is better than microarrays

RNA sequencing (RNA-seq) has over the past few years become the most accurate method for global transcriptome measurements.

mRNA sequencing (mRNA-Seq) detects transcripts and quantifies.

The transcriptome is the complete set of transcripts for a physiological condition

(https://www.gatc-biotech.com/en/expertise/transcriptomics.html)

Command line tools such as SAMtools  and BEDtools are commonly used but user friendly software packages such as RockHopper and NGS-Trex have also been developed.

T-REx is a RNA-seq pipeline. By mining the correlation matrices, k-means clusters and heat maps generated by T-REx, we observed interesting gene-behavior and identified sub-groups in the CodY regulon

statistical and biological analyses of the transcriptome data using tools such as EdgeR, DEseq etc.

Once the factorial design has been made, following steps are i) normalization and scaling of the gene expression values, ii) global analysis of the experiments using e.g., Principal Component Analysis (PCA), iii) differential expression of genes between experiments, iv) clustering of genes expression levels and/or ratios between experiments, v) studying the behavior of groups of genes of interest (classes), vi) functional analysis or gene-set enrichment

T-REx is fed with the four input files, normalization and global analysis of the data will be performed and visualized in several graphs

Current instruments generate more than 500 gigabases in a single run

Input files: Illumina FASTQ reads, SOLiD, Solexa data

Protocol begins with raw sequencing reads and produces a transcriptome assembly

RNAseq analyses steps

Pre-processing; Quality control (excluded reads with a score Q < 20 ); Read mapping  (alignment); Differential expression (DE) analysis; Single nucleotide polymorphism (SNP) analysis 

Gene isoform and splicing variant detection; Networks and function.

(https://ycl6.gitbooks.io/rna-seq-data-analysis/rna-seq_analysis_workflow.html)

* Information on gene network inference methods, miRNA-target predictions,  integration of protein-protein interactions, alternatively splicing variants or promoter enriched sites

RNA Integrity Number (RIN) is the most widely-used approach to assess in vitro RNA degradation

RIN relies on 18S:28S ratio

RNA-seq-based, gene expression profiling studies

transcriptome of stem cell-derived antibiotic selected cardiac bodies

Tools to analyze RNAseq data: C, Python or R 

TopHat and Cufflinks are  tools for gene discovery and comprehensive expression analysis of  RNA-seq data

TopHat: Aligns reads to the genome and discovers transcript splice (based on Bowtie)

Cufflinks: Uses this map against the genome to assemble the reads into transcripts. It clusters transcripts such as  that share the same transcription start site (TSS)

CummeRbund: tool for visualizing RNA-seq analysis results

Trinity,Trans-Abyss,Oases: Gene discovery following de novo transcriptome assembly

CAP-miRSeq: A comprehensive analysis pipeline for deep microRNA sequencing
cDNA sequencing with Sanger sequencers drastically expanded our catalog of known human genes.

sRNA sequencing

to study the role of noncoding RNA in gene silencing and post-transcriptional regulation.

16S rRNA Sequencing

A culture-free method to identify and compare bacteria from complex microbiomes or environments that are difficult to study.

(https://en.novogene.com/next-generation-sequencing-services/gene-regulation/small-rna-sequencing/)

#Analyze RNASeq data 

Ensembl 
DEXSeq package
DEXSeq/python_scripts/dexseq_prepare_annotation.py
preparing the gff