Paired-end sequencing allows users to sequence both ends of a fragment and generate high-quality, alignable sequence data. Paired-end sequencing facilitates detection of genomic rearrangements and repetitive sequence elements, as well as gene fusions and novel transcripts.
A contig--from the word "contiguous"--is a series of overlapping DNA sequences used to make a physical map that reconstructs the original DNA sequence of a chromosome or a region of a chromosome. A contig can also refer to one of the DNA sequences used in making such a map.
The DNA is separated by capillary electrophoresis on the basis of size, and from the order of fragments formed, the DNA sequence can be read. The DNA sequence readout is shown on an electropherogram that is generated by a laser scanner. The method is known as the dideoxy chain termination method.
Typically read count is the total number of reads going into the analysis. It could be based off single or multiple sequencing libraries. Also it can be used to describe the number of reads that align to a region of the reference.
Most of the time, the reason people perform RNA-seq is to quantify gene expression levels. There are two main ways of measuring the expression of a gene, or transcript, or whatever, in RNA-seq data: counts are simply the number of reads overlapping a given feature such as a gene.
Sometimes a distinction is made between sequence coverage and physical coverage. Where sequence coverage is the average number of times a base is read, physical coverage is the average number of times a base is read or spanned by mate paired reads.
True LRS technologies – sometimes referred to as third generation sequencers – directly sequence single molecules of DNA in real time, often without the need for amplification. Both have developed platforms for 'real-time' sequencing of nucleic acids (DNA and RNA) that is faster than current short-read technologies.
The insert size is the library size minus your two read pairs' sizes. This area that is not sequenced between the read pairs.
In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling. RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries.
A typical RNA-seq experiment consists of the following steps:
- Design Experiment. Set up the experiment to address your questions.
- RNA Preparation. Isolate and purify input RNA.
- Prepare Libraries. Convert the RNA to cDNA; add sequencing adapters.
- Sequence. Sequence cDNAs using a sequencing platform.
- Analysis.
Broadly speaking, there are two main steps to aligning RNA-seq data with TopHat2: 1. Align the reads to a reference genome (using Bowtie2). 1 Page 2 RNA-seq: From reads to counts 2 2. Identifying splice junctions and mapping reads to these junctions.
DESEQ2 R Tutorial
- Quality assess and clean raw sequencing data.
- Align reads to a reference.
- Count the number of reads assigned to each contig/gene.
- Extract counts and store in a matrix.
- Create column metadata table.
- Analyze count data using DESEQ2.
- Install packages and load libraries.
- Download data.
Chromatin immunoprecipitation (ChIP) is a type of immunoprecipitation experimental technique used to investigate the interaction between proteins and DNA in the cell. The DNA-protein complexes (chromatin-protein) are then sheared into ~500 bp DNA fragments by sonication or nuclease digestion.
Citation (from within R, enter citation("DESeq2") ): Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550.
The standard protocol for library construction requires between 100 ng and 1 μg of total RNA. There are kits available for ultra-low RNA input that start with as little is 10 pg-10ng of RNA; however, the reproducibility increases considerably when starting with 1-2 ng.
“mRNA-Seq offers improved specificity, so it's better at detecting transcripts, and specifically isoforms, than microarrays. It's also more sensitive in detecting differential expression and offers increased dynamic range.”
As far as I know, DNA sequencing coverage is simply calculated as (read count x read length / genome size). This means, for example, that an experiment might be described as "40x coverage".
Recommendations for RNA-seq experiment designAt least six replicates per condition for all experiments. At least 12 replicates per condition for experiments where identifying the majority of all DE genes is important. For experiments with <12 replicates per condition; use edgeR (exact) or DESeq2.
Library SizeThe RNA that was sequenced is called the RNA library. With longer read lengths and more accurate sequencing, these days in most organisms, most of the reads are mapped. Library size could mean one of two things: the total number of reads that were sequenced in the run or the total number of mapped reads.
RNA-seq involves conversion of a sample of RNA to a cDNA library, which is then sequenced and mapped against a reference genome. In addition to the ability to measure the level of gene expression, it provides further information on alternative splicing and non-coding RNA (such as microRNA) (Chaussabel et al., 2010).
One of the first considerations for planning an RNA sequencing (RNA-Seq) experiment is the choosing the optimal sequencing depth. As described in our article on NGS coverage calculation, the term sequencing depth describes the total number of reads obtained from a high-throughput sequencing run.
Reads Passing Filter **
| MiSeq Reagent Kit v2 | MiSeq Reagent Kit v3 |
|---|
| Single Reads | 12-15 million | 22–25 million |
| Paired-End Reads | 24–30 million | 44–50 million |
The RNA content of a cell provides direct knowledge of gene regulation and protein content information. Transcriptome sequencing, also called RNA sequencing, refers to the sequencing of cDNA to get information about a sample's total RNA content at a given time in a given condition under study.
This approach, called "RNA-seq", can generate quantitative expression scores that are comparable to microarrays, with the added benefit that the entire transcriptome is surveyed without the requirement of a priori knowledge of transcribed regions.
Nonetheless, RNA sequencing is still expensive and time-consuming, because it first requires the costly preparation of an entire genomic library -- the DNA pool generated from the RNA of cells -- while the data itself are also difficult to analyze.
1. Messenger RNA (mRNA) carries the genetic information copied from DNA in the form of a series of three-base code “words,” each of which specifies a particular amino acid. 2. Transfer RNA (tRNA) is the key to deciphering the code words in mRNA.
Sequencing of RNA (RNA-seq) is a recent technique that emerged shortly after next-generation sequencing (NGS) was invented approx. 10 years ago and since has revolutionized biological research in the 21st century.
Ribonucleic acid (RNA) is a linear molecule composed of four types of smaller molecules called ribonucleotide bases: adenine (A), cytosine (C), guanine (G), and uracil (U).
While sequencing DNA gives a genetic profile of an organism, sequencing RNA reflects only the sequences that are actively expressed in the cells. To sequence RNA, the usual method is first to reverse transcribe the RNA extracted from the sample to generate cDNA fragments.
