sequencing:使用二代测序原因:高通量,短序列 不用长序列原因: 1.算法错误率高 2.长序列测序将嵌合体基因错误积累。嵌合体基因:通过重组由来源与功能不同的基因序列剪接而形成的杂合基因

sequencing:

增多的total length>N>gap>missing in genome The reads with a frequency >

1 were called duplicated reads, and we defined the duplication rate as the count of duplicated reads / the count of total reads. 利用further reads mapping gap后仍旧存在的gap,原因是:此区域大部分是串联重复区域和转座子 串联重复区域是什么? the non-interspersed repeat sequences using RepeatMasker with the “-noint” option, including Simple_repeat, Satellites, and Low_complexity repeats panda装配过程中失掉了一部分串联重复 Loose tandem repeats, requiring that percent of matches larger than 90% and percent of Indels less than 10%; Exact tandem repeats, requiring that percent of matches equal to 100% and percent of Indels equal to 0%

evaluate-1:

1.realigned all the usable sequencing reads onto the scaffolds using SOAPaligner

2.CG content:

3. Comparison of assembled scaffolds and 26 panda mRNA gene sequences in GenBank: The known panda mRNA gene sequences were downloaded from GenBank

4. sequenced and assembled nine BACs independently using Sanger sequencing technology

5.

evaluate-2:

1.annotation 2.mannual check

estimated the genome size:

L为reads平均长度,K为k-mer长度;knum为所有的k-mer总个数,kdepth为k-mer频数的期望深度(即k-mer曲线中主峰对应的横坐标位置);bnum为测序reads覆盖碱基的总个数,bdepth为覆盖碱基的期望深度。 L=average_reads_length= 52 bp K=17 kdepth=k-mer曲线中主峰对应的横坐标位置=15 bdepth为覆盖碱基的期望深度 bnum为测序reads覆盖碱基的总个数 为什么用C值? We therefore used C-values (haploid DNA content in picograms), as this is proportional to genome size

repetitive sequences:

1.the Repbase transposable-element consensus sequences were annotated using mammalian genomes other than the panda.

2.about 70Mb of transposable-element sequences (3% of the genome) had a ,10% divergence rate from the consensus (Supplementary Fig. 9), which are likely to be active transposable elements of recent origin.

Comparative genomics-Sequence comparison:

1.segmental duplication:也称为节段重复,是真核基因组内高度同源的序列元件。 whole-genome assembly comparison (WGAC)受 assembly whole-genome shotgun sequence detection or WSSD:Considering the average depth was about 2.47 times that of the whole genome, we estimated that the total length of the duplicated copies was about 34.3 Mb 2.Conserved sequences among the panda, dog and human genomes.Each of these genomes contained ,1.4 Gb of non-repetitive sequence, 3.The <target> dog or human and <query>panda are usually just the names of files containing the sequences to be aligned, in either FASTA, Nib, or 2Bit format. However they can be HSX index files that refer to the sequences indirectly, and they also can specify pre-processing actions such as selecting a subsequence from the file (see Sequence Specifiers for details). With certain options such as ‑‑self the <query> is not needed; otherwise if it is left unspecified the query sequences are read from stdin (though this does not work with random-access formats like 2Bit). As a special case, the <target> is omitted when the ‑‑targetcapsule option is used, since the target sequence is embedded within the capsule file. 4.there were 4–5 times more rearrangements in dog than in panda, which provided evidence that the panda has a lower divergence rate than the dog

annotation:

1.predict/annotation “With synteny” means genes predicted on regions with synteny to the human or dog, and the fragmental genes were conjoined by building gene-scaffolds. “Out of synteny” means genes predicted on regions without synteny evidence to the other species; Pseudo-genes, are those containing more frame errors than a specified threshold. 2.验证: 3.The panda was similar to the human with respect to all of these key parameters 4.Figure S11 | Analysis of sequence completeness of the predicted genes. a.Alignment rate comparison between panda (annotation后的)and dog using single-copy genes, both panda and dog genes were aligned to human genes, and the alignment rate was calculated for each pair of orthologous genes. On average, the dog and panda genes covered 96.2% and 93.5% of the human gene sequences, b. The ratio of missing exons(未annotation的). The annotated panda genes were compared with the human genes, and the ratio of missing length was calculated on 5’-end, 3-end, and middle of genes unannotated exons were at the 5‘ or 3’ ends of genes; these exons were usually very small conclusion:quality of the predicted panda genes was comparable to that of other well-annotated mammalian genomes.

Comparative genomics-PSGs prediction:

1.one specific for the panda lineage, one specific for the dog lineage, and one combining evidence from all five species included in the alignmen 2. 3.The panda and the dog lineage only share six PSGs: MWU&PE consistent with the results from previous genome-wide positive selection scans in mammalian genomes, 4.

Panda-specific characteristics:

diet:

1.在遗传学中,Ka/Ks或者dN/dS表示的是异意替换(Ka)和同意替换(Ks)之间的比例。这个比例可以判断是否有选择压力作用于这个蛋白质编码基因。   不导致氨基酸改变的核苷酸变异我们称为同义突变,反之则称为非同义突变。一般认为,同义突变不受自然选择,而非同义突变则受到自然选择作用。在进化分析中,了解同义突变和非同义突变发生的速率是很有意义的。常用的参数有以下几种:同义突变频率(Ks)、非同义突变频率(Ka)、非同义突变率与同义突变率的比值(Ka/Ks)。如果Ka/Ks>1,则认为有正选择效应。如果Ka/Ks=1,则认为存在中性选择。如果Ka/Ks<1,则认为有纯化选择作用。 移码突变是由DNA序列中的许多核苷酸的indel引起的遗传突变,其不能被3整除。由于密码子基因表达的三重特性,插入或缺失可以改变阅读框,导致与原始翻译完全不同。序列中较早的缺失或插入发生,蛋白质的改变越多。

2.The third exon contained a 2-bp (‘GG’) insertion; the sixth exon contained a 4-bp (‘GTGT’) deletion. 3.coding not protein:purify selection: 如果某DNA突变对于生物是有害的,但是却不是致命的(立即被消灭),那么这个突变就将处于纯化选择作用之下。 weak selection can lead to different evolutionary fates due to different population sizes and reduced population size can increase the fixation of slightly deleterious mutations. low fecundity rate: a.Alignment b.Phylogenies of FSHB genes in different mammalian species

Population genetics:

1.杂合率:a. panda vs human;b.常 vs X 2.Substitution matrix of the panda heterozygous SNPs in the whole genome. The ratio of transition / transversion is 2.1.

The sequence and de novo assembly of the giant panda genome.ppt的更多相关文章

  1. DISCOVAR de novo

    海宝建议用这个拼接软件 http://www.broadinstitute.org/software/discovar/blog/?page_id=98 DISCOVAR – variant call ...

  2. (转)8 reviews about de novo genome assembly

    转自:http://dskernel.blogspot.com/2012/04/8-reviews-about-de-novo-genome-assembly.html 8 reviews about ...

  3. De novo RNA-Seq Assembly Using De Bruijn Graphs

    De novo RNA-Seq Assembly Using De Bruijn Graphs  2017-06-12 09:42:47     59     0     0 在说基因组的拼接之前,可 ...

  4. HHP|HPLC-MS/MS|PMT|PST|de novo|

    生物医学大数据 Protein 应用 人类蛋白质组计划 Gene的存在要依靠在蛋白水平确认基因真实存在. 蛋白质组是确定时间地点的研究单元的蛋白质总体,因为时间.地点和研究单元的相互组合存在多种变化, ...

  5. chromosome interaction mapping|cis- and trans-regulation|de novo|SRS|LRS|Haplotype blocks|linkage disequilibrium

    Dissecting evolution and disease using comparative vertebrate genomics-The sequencing revolution   s ...

  6. De novo 测序基础知识

    名词解释 De novo:拉丁文,从头开始的意思,de nove测序则是指在不需要任何参考序列的情况下对某一物种进行基因组测序,然后将测得的序列进行拼接.组装,从而绘制该物种的全基因组序列图谱. 重测 ...

  7. 全基因组测序 从头测序(de novo sequencing) 重测序(re-sequencing)

    全基因组测序 全基因组测序分为从头测序(de novo sequencing)和重测序(re-sequencing). 从头测序(de novo)不需要任何参考基因组信息即可对某个物种的基因组进行测序 ...

  8. MCP|ZWT|Precision de novo peptide sequencing using mirror proteases of Ac-LysargiNase and trypsin for large-scale proteomics(基于Ac-LysargiNase和胰蛋白酶的蛋白组镜像de novo测序)

    一.概述 由于难以获得100%的蛋白氨基酸序列覆盖率,蛋白组de novo测序成为了蛋白测序的难点,由Ac-LysargiNase(N端蛋白酶)和胰蛋白酶构成的镜像酶组合可以解决这个问题并具有稳定性, ...

  9. Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework (使用序列掩码搜索结合肽段从头测序框架发现了数千个新肽段)-解读人:刘佳维

    期刊名:Molecular & Cellular Proteomics 发表时间:(2019年12月) IF:4.828 单位: 朱拉隆功大学 费城威斯塔研究所 物种:人 技术:de novo ...

随机推荐

  1. openstack trove weekly meeting时间即将更改

    为了平衡英国.巴黎.德国.美国和中国开发者的作息习惯,openstack trove项目组在5月18日的weekly meeting上开始讨论新的开会时间. 当前的开会时间是,周三 UTC 18:00 ...

  2. 连接mysql的各种方式

    mysql连接操作是客户端进程与mysql数据库实例进程进行通信.从程序设计角度来说,属于进程通信,常用进程通信包括: 管道.Tcp/Ip 套接字.UNIX域套接字. 1.TCP/IP (1)使用最多 ...

  3. rabbitmq安装及简单demo练习

    参考:https://my.oschina.net/loveorange/blog/3026473 安装参考链接: 1. 下载自己需要的rabbitmq_server(http://www.rabbi ...

  4. linux tar/ tar.gz文件解压

    1.tar 压缩 tar -cvf jpg.tar *.jpg //将目录里所有jpg文件打包成tar.jpg tar -czf jpg.tar.gz *.jpg   //将目录里所有jpg文件打包成 ...

  5. 视图家族之视图工具集viewsets

    视图家族之视图工具集viewsets 一.视图集ViewSet 使用视图集ViewSet,可以将一系列逻辑相关的动作放到一个类中: list() 提供一组数据 retrieve() 提供单个数据 cr ...

  6. EOF是什么?(笔记)

    一.参考文章 1.EOF是什么?(阮一峰网络日志) 2.Linux 中的 EOF 到底是什么 二.知识点 1.EOF 定义在 /usr/include/stdio.h 文件中: 从上面 EOF 的定义 ...

  7. Java自学-泛型 泛型转型

    Java 中的子类泛型转型成父类泛型 步骤 1 : 对象转型 根据面向对象学习的知识,子类转父类 是一定可以成功的 package generic; import charactor.ADHero; ...

  8. UML-SSD-为什么要画SSD?

    需求文本看着过于抽象,采用SSD一目了然. 在设计软件之前,分析人员会关注系统会发生那些事件? 1.基本上,软件系统要对以下3种事件进行响应: 1).来自于参与者(人或计算机)的外部事件 2).时间事 ...

  9. java通过HSSFWorkbook导出xls文件

    使用swgger2.Restlet等接口工具有bug导致导出失败,测试直接使用浏览器 //导出列表-新 @UserRoleJudgment(authpos = SystemControllerLog. ...

  10. dubbo的重试原则

    验证思路.使用超时来验证重试次数 XML 注解