Mapped read depth refers to the total number of bases sequenced and aligned at a given reference base position (note that "mapped" and "aligned" are used interchangeably in the sequencing community).
However, such analyses presume a fixed true-positive set of transcripts or binding locations, the recovery of which is increased with increased sequencing depth. Care must be taken when dealing with heterogeneous samples, as the true set may be cell type specific.
In real-world sequencing approaches, read lengths are short (that is, ≤250 nucleotides) and can contain sequence errors. When considered alone, an error is indistinguishable from a sequence variant.
Upon commencing any next-generation sequencing experiment it is difficult to predict the level at which samples should be sequenced. For example, the detection of lowly expressed transcripts and rare splice events in RNA sequencing requires very deep sequencing.
There are two sequencing read types: single-read and paired-end sequencing. Single-read sequencing involves sequencing DNA fragments from one end to the other. It is useful for some applications, such as small RNA sequencing, and can be a fast and economical option.
Because long reads allow for more sequence overlap, they are useful for de novo assembly and resolving repetitive areas of the genome with greater confidence. For other applications, such as expression profiling or counting studies, ...
Coverage depth refers to the average number of sequencing reads that align to, or "cover," each base in your sequenced sample. The Lander/Waterman equation 1 is a method for calculating coverage (C) based on your read length (L), number of reads (N), and haploid genome length (G): C = LN / G
The average depth of sequencing coverage can be defined theoretically as LN/G, where L is the read length, N is the number of reads and G is the haploid genome length.
The process of identifying consistent differences between the sequenced reads and the reference genome; these differences include single base substitutions, small insertions and deletions, and larger copy number variants.
DNA resequencing explores genetic variation in individuals, families and populations, particularly with respect to human genetic disease. Requirements for sequencing depth in these studies are governed by the variant type of interest, the disease model and the size of the regions of interest. Resequencing can reveal single-nucleotide variants (SNVs), small insertions and deletions (indels), larger structural variants (such as inversions and translocations) and copy number variants (CNVs). Naturally, the design of a particular study depends on the biological hypothesis in question, and different sequencing strategies are used for population studies compared with those for studies of Mendelian disease or of somatic mutations in cancer. Furthermore, targeted resequencing approaches allow a trade-off between sequencing breadth and sample numbers: for the same cost, more samples can be sequenced to the same depth but over a smaller genomic region. Here, we discuss the merits of whole-genome sequencing (WGS) relative to targeted resequencing approaches, including WES, in the context of these different variant types and disease models.
Hybrid sequencing approaches are being introduced to overcome problems in genome assembly and in placing highly repetitive sequence in a genome.
In a typical experiment, nucleic acid fragments that are involved in an interaction are isolated and are subjected to high-throughput sequencing. The resulting reads are regarded as tags that can be used to quantify distinct molecules in the sample. In this case, the read length and the error rate only need to be sufficient to distinguish between the different molecules, for example, to unambiguously identify a location in the genome. The number of reads that map to a particular nucleotide is the primary quantity of interest and is used to estimate the abundance of molecules sequenced. Thus, the required sequencing depth depends on the number of true genomic locations. In the case of ChIP–seq experiments for transcription factor binding, such depth is often unknown at the outset, although it may be known, for example, when comparing methylation profiles between cell types.
In real-world sequencing approaches, read lengths are short (that is, ≤250 nucleotides) and can contain sequence errors. When considered alone, an error is indistinguishable from a sequence variant. This problem can be overcome by increasing the number of sequencing reads: even if reads contain a 1% variant-error rate, the combination of eight identical reads that cover the location of the variant will produce a strongly supported variant call with an associated error rate of 10 −16 (Ref. 3 ). Increased depth of coverage therefore 'rescues' inadequacies in sequencing methods ( Box 1 ). Nevertheless, generating greater depth of short reads does not cure all sequencing ills. In particular, it alone cannot resolve assembly gaps that are caused by repetitive regions with lengths that either approach or exceed those of the reads. Instead, in the paired-end read approach, paired reads — two ends of the same DNA molecule that are sequenced and which are separated by a known distance — are used to unambiguously place repetitive regions that are smaller than this distance.
The ENCODE project's guidelines for ChIP–seq experiments suggest that point-source factor experiments should use 20 million reads per factor, summed across replicates, in mammals or two million reads per factor in organisms with smaller genomes, such as the fruitfly and the nematode worm 79. However, at this level most of the factors assayed have not reached saturation in the numbers of peaks identified 79, 57, and saturation is not achieved even at 55 million reads, or 100 million reads for some factors, in human cells. In a study of the smaller fruitfly genome, it was found that peak identification for one transcription factor started to show signs of saturation at 16.2 million reads, which is equivalent to ~327 million reads in humans 81, although the numbers of reproducible peaks between multiple replicates started to saturate at 5.4 million reads (and at ~110 million reads in humans).
This is the total amount of sequence data produced by the instrument (pre-alignment), divided by the reference genome size. Although raw read depth is often provided by sequencing instrument vendors as a specification, it does not take into account the efficiency of the alignment process.
The mean mapped read depth (or mean read depth) is the sum of the mapped read depths at each reference base position, divided by the number of known bases in the reference. The mean read depth metric indicates how many reads, on average, are likely to be aligned at a given reference base position.
A high IQR indicates high variation in coverage across the genome, while a low IQR reflects more uniform sequence coverage. In the example histograms above, the lower IQR indicates that the histogram on the left has better sequencing coverage uniformity than that on the right.
The IQR is the difference in sequencing coverage between the 75th and 25th percentiles of the histogram. This value is a measure of statistical variability, reflecting the non-uniformity of coverage across the entire data set. A high IQR indicates high variation in coverage across the genom e, while a low IQR reflects more uniform sequence ...
For RNA sequencing, read depth is typically used instead of coverage. Detecting low-expression genes can require an increase in read depth. The ENCODE project (updated here ) has data standards for RNA-Seq and Small RNA sequencing that are an excellent resource for many projects.
Experiments looking to get an in-depth view of the transcriptome, or to assemble new transcripts, may require 100–200 million reads. In these cases, researchers may need to sequence multiple samples across several high output sequencing lanes.
Read depth varies depending on the goals of the RNA-Seq study. Most experiments require 5–200 million reads per sample, depending on organism complexity and size, along with project aims.
To determine how many samples can be run at one time, divide the number of reads produced by the flow cell by the number of reads needed per sample: number of reads per flow cell / number of reads per sample=number of samples per flow cell.
Small RNA Analysis – Due to the short length of small RNA, a single read (usually a 50 bp read) typically covers the entire sequence. A read length of 50 bp sequences most small RNAs, plus enough of the adapter to be accurately identified and trimmed during data analysis.