The contiguous sequence generated by the polymerase during sequencing is referred to as a “polymerase read” or a Continuous Long Read (CLR).
This CLR read may include sequence from adapters and multiple copies of inserts, because it traverses the circular template many times. The CLRs are processed to remove adapter sequences and to retain only the insert sequence, called “subreads”.
All other sequences sequenced from the CLR are called “scraps”.
Multiple copies of subreads generated from the single SMRTBell can then be collapsed to a single, high-quality sequence, called the “read of insert” ROI or Circular Consensus Sequence (CCS).
The scraps file includes all the sequence not in the HQ (high quality) region, and the adapter sequences. The old bax format saved everything with indexes into the usable data. The subreads.bam contains all the usable data.
关于scrapes和subreads的bam文件的区别，只用subreads即可 The scraps file includes all the sequence not in the HQ (high quality) region, and the adapter sequences. The old bax format saved everything with indexes into the usable data. The subreads.bam contains all the usable data.
.subreads.bam A file contain usable reads without adapter;The Sequel System outputs one subreads.bam file per collection/cell, which contains unaligned base calls from high-quality regions.
.scraps.bam A file contain sequence data outside of the High Quality region, rejected subreads,excised adapter and possible barcode sequences, as well as spike-in control sequences. (The basecaller marks regions of single molecule sequence activity as high-quality.)
The goal of styler is to provide non-invasive pretty-printing of R source code while adhering to the tidyverse formatting rules. styler can be customized to format code according to other style guides too.
Calculate the expected genotypic counts under Hardy-Weinberg Equilibrium, and then calculate the excess or deficiency of homozygotes in each subpopulation. Summary of homozygote deficiency or excess relative to HWE: Pop. 1. Observed = Expected: perfect fit Pop. 2. Excess of 15.5 homozygotes: some inbreeding Pop. 3. Deficiency of 45 homozygotes: outbred or experiencing a Wahlund effect (isolate breaking).
Expected allele frequency
500*0.5^2 = 125 (= observed)
100*0.65^2 = 42.25 (observed has excess of 7.75)
1,000*0.35^2 = 122.5 (observed has deficiency of 22.5)
50020.5*0.5 = 250 (= observed)
10020.65*0.35 = 45.5 (observed has deficit of 15.5)
1,00020.65*0.35 = 455 (observed has excess of 45)
500*0.5^2 = 125 (= observed)
100*0.35^2 = 12.25 (observed has excess of 7.75)
1,000*0.35^2 = 422.5 (observed has deficiency of 22.5)
Calculate the local observed heterozygosities of each subpopulation (we will call them Hobs s, where the s subscript refers to the sth of n populations – 3 in this example).
Local observed heterozygosities
250/500 = 0.5 (Hobs 1)
30/100 = 0.3 (Hobs 2)
500/1000 = 0.5(Hobs 3)
Calculate the local expected heterozygosity, or gene diversity, of each subpopulation Hexp = 2pq
Local expected heterozygosity
20.50.5=0.5 (Hexp 1)
20.653.5=0.455 (Hexp 2)
20.350.65=0.455 (Hexp 2)
Calculate the local inbreeding coefficient of each subpopulation F = (Hexps -Hobs)/Hexp [positive F means fewer heterozygotes than expected indicates inbreeding] [negative F means more heterozygotes than expected means excess outbreeding]
Step 6. and 7.
Calculate p-bar (p-bar, the frequency of allele A) over the total population. Calculate q-bar (q-bar, the frequency of allele a) over the total population. Check: p-bar + q-bar = 1.0
the frequency of allele over the total population
Calculate the global heterozygosity indices (over Individuals, Subpopulations and Total population) HI based on observed heterozygosities in individuals in subpopulations HS based on expected heterozygosities in subpopulations HT based on expected heterozygosities for overall total population
HT (in overall total population)
2*p-bar *q-bar = 2 * 0.4156 * 0.5844 = 0.4858
Calculate the global F-statistics Compare and contrast the global FISbelow with the “local inbreeding coefficient” Fs of Step 5. Here we are using a weighted average of the individual heterozygosities over all the subpopulations. Both FIS and Fs are, however, based on the observed heterozygosities, whereas FST and FIT are based on expected heterozygosities.
Finally, draw some conclusions about the genetic structure of the population and its subpopulations. 1) One of the possible HWE conclusions we could make: Pop. 1 is consistent with HWE (results of Step
Two of the possible “local inbreeding” conclusions we could make from Step 5:
Pop. 2 is inbred (results of Step 5), and
Pop. 3 may have disassortative mating or be experiencing a Wahlund effect (more heterozygotes than expected).
Conclusion concerning overall degree of genetic differentiation (FST)
Subdivision of populations, possibly due to genetic drift, accounts for approx. 3.4% of the total genetic variation (result of Eqn FST.8 FST calculation in Step 9),
No excess or deficiency of heterozygotes over the total population (FIT is nearly zero).
I always use gtf file and retrieve gene information. There isn’t a highly flexible tool to solve my demand. I modified the code from “https://github.com/Jverma/GFF-Parser”, thanks Jverma. This tool will be easier to use.
Basically, there are three parameters.
id: either transcript id or gene id.
attType: attribute defined in gtf file. E.g. feature (column 3), gene_name, exon_number, transcript_id in column 9
attValue: the attribute value you want to search for.
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>>> import sys >>> from gtfParser import gtfParser
>>> gtf = gtfParser("example.gtf")
>>> # Get all exons in CDK7 >>> gtf.getRecordsByID("CDK7", "feature", "exon")
>>> # Get all features of transcript_id defined as "NM_001324069" in gene "CDK7" >>> gtf.getRecordsByID("CDK7", "transcript_id", "NM_001324069")
>>> # Get start codon where feature was defined as "start_codon" in transcript "NM_001324069" >>> gtf.getRecordsByID("NM_001324069", "feature", "start_codon")
>>> # Get a exon where its id is "NM_001324078.1" in "NM_001324078" transcript >>> gtf.getRecordsByID("NM_001324078", "exon_id", "NM_001324078.1")
我是在这篇文章（Integrative pathway enrichment analysis of multivariate omics data ）中遇到的合并多个p-value的操作。这篇文章是今年发表在NC上。所有的组学或者大规模的数据分析，都需要探索数据背后相关的生物学功能，所以通路富集分析非常普遍。通常的做法是基于单一组学、单一数据集的数据进行分析，随着生物学数据的爆发，大规模多组学数据变得普遍，这篇文章介绍了基于整合的多组学或多数据集的数据进行通路分析的工具ActivePathways。