Introduction

Resequencing 重測序

[NIBI] Resequencing
https://www.ncbi.nlm.nih.gov/probe/docs/techresequencing/

Resequencing of candidate genes or other genomic regions of interest in patients and controls is a key step in detection of mutations associated with various congenital diseases. Resequencing techniques can be divided into those which test for known mutations (genotyping) and those which scan for any mutation in a given target region (variation analysis). Typical mutations being tested are substitution (SNP), insertion and deletion mutations.

對象
候選基因（candidate genes）和其他基因區(qū)域（ interest in patients and controls ？）

用途
檢測與多種先天疾?。╟ongenital diseases）相關(guān)突變。突變側(cè)重于檢測替換（snp），插入（insertion）， 刪除（deletion）

分類

1. 檢測已知的突變（基因型， genotype）
2. 掃描給定目標(biāo)區(qū)域的所有突變（variation analysis）

步驟
-TODO

Sequence Aligment 序列對比

[WIKIBOOK] Next Generation Sequencing (NGS)/Alignment
https://en.wikibooks.org/wiki/Next_Generation_Sequencing_(NGS)/Alignment

Alignment, also called mapping,[1]
of reads is an essential step in re-sequencing. Having sequenced an organism of a species before, and having constructed a reference sequence, re-sequencing more organisms of the same species allows us to see the genetic differences to the reference sequence, and, by extension, to each other. Alignments of data from these re-sequenced organisms is a relatively simple method of detecting variation in samples. There are certain instances (such as new genes in the sequenced sample that are not found in the existing reference sequence) that can not be detected by alignment alone; however, while other approaches, such as de novo assembly, are potentially more powerful, they are also much harder or, for some organisms, impossible to achieve with current sequencing methods.

序列對比（sequence alignment ）， 也叫序列匹配（sequence mapping）。
是 resequencing 的重要的一個步驟

用途
在已有某物種的測序結(jié)果的前提下（已經(jīng)有的叫參照序列， refrerence sequence）， 再檢測同物種下更多的個體（organisms）。 用于對法發(fā)現(xiàn)個體之間基因的不同

基本思想
檢測序列之間的相似性， 發(fā)現(xiàn)生物序列中的功能，結(jié)構(gòu)和進(jìn)化的信息

分類

• 雙序列對比
• 多序列對比
• 局部對比
• 全局對比

算法

• 取代矩陣
• 空位罰分

局限性
-TODO

de-novo assembly

de novo assembly

存儲格式

SAM

相關(guān)軟件

Paste_Image.png

這篇文章是介紹BWA的 前半部分是分析

Bowtie 2

Bowertie 2 Home
http://bowtie-bio.sourceforge.net/bowtie2/index.shtml

Bowertie 2手冊
http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml#what-is-bowtie-2

Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters to relatively long (e.g. mammalian) genomes. Bowtie 2 indexes the genome with an FM Index (based on the Burrows-Wheeler Transform or BWT) to keep its memory footprint small: for the human genome, its memory footprint is typically around 3.2 gigabytes of RAM. Bowtie 2 supports gapped, local, and paired-end alignment modes. Multiple processors can be used simultaneously to achieve greater alignment speed. Bowtie 2 outputs alignments in SAM format, enabling interoperation with a large number of other tools (e.g. SAMtools, GATK) that use SAM. Bowtie 2 is distributed under the GPLv3 license, and it runs on the command line under Windows, Mac OS X and Linux.
Bowtie 2 is often the first step in pipelines for comparative genomics, including for variation calling, ChIP-seq, RNA-seq, BS-seq. Bowtie 2 and Bowtie (also called "Bowtie 1" here) are also tightly integrated into some tools, including TopHat: a fast splice junction mapper for RNA-seq reads, Cufflinks: a tool for transcriptome assembly and isoform quantitiation from RNA-seq reads, Crossbow: a cloud-enabled software tool for analyzing resequencing data, and Myrna: a cloud-enabled software tool for aligning RNA-seq reads and measuring differential gene expression.

SOAP 2

BWA

Overview

Based on FM-index (Burrows-Wheeler Transform plus auxillary data
structures) which enables fast exact matching.
Short-read algorithm: alter the read sequence such that it matches
the reference exactly.
Long-read algorithm (BWA-SW): sample reference subsequences and
perform Smith-Waterman alignment between the subsequences and
the read.
Work for Illumina and SOLiD single-end (SE) and paired-end (PE)
reads; new component BWA-SW for 454/Sanger SE reads.

Key Features

Fast and moderate memory footprint (<4GB)
SAM output by default
Gapped alignment for both SE and PE reads
Effective pairing to achieve high alignment accuracy; suboptimal hits
considered in pairing.
Non-unique read is placed randomly with a mapping quality 0; all hits
can be outputted in a concise format.
Guarantee to find k-difference in the seed (first 32bp by default).
The default configuration works for most typical input.
Automatically adjust parameters based on read lengths and error rates.
Estimate the insert size distribution on the fly