Sci-Hub 最新可用网址

Pubmedy:显示影响因子+引用数、Sci-hub全文下载的浏览器扩展

很多人都知道一个下载文献的好地方 Sci-Hub,但是现在 Sci-Hub 的站长 Alexandra Elbakyan 女士面临着爱思唯尔的诉讼,让其面临数百万美元的赔偿。因此现在 Sci-Hub 在某些时候可能不可用了。不过 Alexandra Elbakyan 女士并未妥协,希望大家能多支持一下这位女士(貌似只能接受比特币捐款,我也不知道怎么弄,要是paypal或者payoneer就好了)。

相信很多人都知道下面这两个网址:

Sci-Hub 官网:http://www.sci-hub.org/

Sci-Hub 替代网址:http://www.sci-hub.io/

http://sci-hub.ac/和http://sci-hub.cc/也都已经阵亡(2017月12月6日测试)

以上几个网址都挂了,现在可用的是

http://sci-hub.bz/(12月8日测试 已经不可用了)

http://sci-hub.la/

http://sci-hub.hk/

http://sci-hub.tw/

http://sci-hub.tv/

http://80.82.77.83/

http://80.82.77.84/

fasta2nexus by R script

Workspace loaded from ~/.RData]

> setwd("/home/shenzy/work/beast/51samples")
> library(seqinr)
> data=read.fasta("51strain_core_gene_alignment.aln")
> library(ape)

Attaching package: ‘ape’

The following objects are masked from ‘package:seqinr’:

    as.alignment, consensus

> write.nexus.data(data,file="51strain_core_gene_alignment.aln.nexus", format="DNA")
>

change the font size of leaf nodes when generating phylogenetic trees using Bio.Phylo.draw()

axes : matplotlib/pylab axes If a valid matplotlib.axes.Axes instance, the phylogram is plotted in that Axes. By default (None), a new figure is created.

This means that you can load your own axes with your size of choice. For example

import matplotlib
import matplotlib.pyplot as plt
from Bio import Phylo
from cStringIO import StringIO

def plot_tree(treedata, output_file):
    handle = StringIO(treedata)  # parse the newick string
    tree = Phylo.read(handle, "newick")
    matplotlib.rc('font', size=6)
    # set the size of the figure
    fig = plt.figure(figsize=(10, 20), dpi=10[......]

Read more

How to find large files with size in Linux? find and du command example

One of the common problem while working in UNIX is to find large files to free some space. Suppose, your file system is full and you are receiving an alert to remove spaces or if your host is run out of space and your server is not starting up, the first thing you do is find top 10 largest files and see if you can delete them. Usually, old files, large Java heap dumb are good candidates for removal and freeing up some space. If you are running Java application e.g. core java based programs or web application running on Tomcat then you can remove those heap dump files and free some space, but t[……]

Read more

y叔的ChIP-seq数据分析大礼包(转贴)

熟悉我们生信技能树团队的应该都知道大名鼎鼎的y叔啦,作为我们论坛的荣誉顾问,y叔一直勤勤恳恳的指出我们的错误,特意在此谢谢y叔!并奉上y叔的ChIP-seq数据分析大礼包,已经征得y叔同意啦!

关注Y叔微信公众账号biobabble

CS0: ChIPseq从入门到放弃

接下来要出一个ChIPseq系列,讲一讲ChIPseq和我的ChIPseeker,从入门到放弃是我自己的个人写照。我做ChIPseq总共也就3个月的时间,做的事情并不多,在一知半解的情况下写下了ChIPseeker包。

正如我在《话题投票》里说的,我当时被要求做ChIPseq分析是为他人做嫁衣,而且是完全白干那种,但做为学生,白干也得干。

当时一开始使用ChIPpeakAnno做注释,但用UCSC genome browser检验结果的时候,发现对不上。在对ChIPpeakAnno包不满意的情况下,开始着手写ChIPseeker,其实在使用ChIPpeakAnno的时候,我就有写代码对结果做一些可视化,所以未有ChIPseeker先有ChIPseeker的部分可视化功能。当时写了篇博客文说ChIPpeakAnno的问题,一个月后就在Bioconductor上发表了ChIPseeker,这包完全是我半夜在宿舍里写出来的。

当时还在生物系,被我炒掉的前老板每天要求必须起码在实验[……]

Read more

ChIPseeker for ChIP peak annotation (转贴)

https://guangchuangyu.github.io/2014/04/chipseeker-for-chip-peak-annotation/

ChIPpeakAnno WAS the only R package for ChIP peak annotation. I used it for annotating peak in my recent study.

I found it does not consider the strand information of genes. I reported the bug to the authors, but they are reluctant to change.

So I decided to develop my own package, ChIPseeker, and it’s now available in Bioconductor.

> require(TxDb.Hsapiens.UCSC.hg19.knownGene)
> txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
> require(ChIPseeker)
> peakfile = getSampleFiles()[[4]]
> peakfile[......]

Read more

Chip-seq流程报告(转贴)

一、摘要

实验旨在了解Chip-seq的基本原理。通过模仿文献《Targeting super enhancer associated oncogenes in oesophageal squamous cell carcinoma》的流程,学会利用NCBI和EBI数据库下载数据,熟悉Linux下的基本操作,并使用R语言画图,用Python或者shell写脚本进行基本的数据处理,通过FastQC、Bowtie、Macs、samtools、ROSE等软件进行数据处理,并对预测结果进行分析讨论。

二、材料

1、硬件平台

处理器:Intel(R) Core(TM)i7-4710MQ CPU @ 2.50GHz 2.50GHz

安装内存(RAM):16.0GB

2、系统平台

Windows 8.1,Ubuntu

3、软件平台

① Aspera connect ② FastQC ③ Bowtie

④ Macs 1.4.2 ⑤ IGV ⑥ ROSE

4、数据库资源

NCBI数据库:https://www.ncbi.nlm.nih.gov/;

EBI数据库:http://www.ebi.ac.uk/;

5、研究对象

加入H3K27Ac 抗体处理过的TE7细胞系测序数据和其空白对照组

加入H3K27Ac 抗体处理过的KYSE510细胞系和其空[……]

Read more

Dating a node with BEAST2.0

For details, read http://beast2.cs.auckland.ac.nz/index.php/Main_Page and http://beast2.cs.auckland.ac.nz/index.php/FAQ . For any problem, do not hesitate to browse through the list of questions on the BEAST forum https://groups.google.com/forum/#!forum/beast-users .

Exercise description

The exercise is mainly based on the Divergence Dating tutorial, but also includes a few screen captures.

Sequences files for this exercise are taken from a very inspiring published work
Schnitzler, J., T.G. Barraclough, J.S. Boatwright, P. Goldblatt, J.C. Manning, M.P. Powell, T. Rebelo, and[……]

Read more

Chip seq

何謂ChIP-Seq?

ChIP–seq ( Chromatin immunoprecipitation sequencing )是指染色質免疫沉澱後,所獲得的DNA片段進行高通量定序,並將此片段利用生物資訊的軟體對回至基因體,可以瞭解DNA-binding proteinshistone modifications的狀況,進而得知染色结合的調控因子的相互作用關係。

ChIP-chipChIP-Seq差異?

次世代定序較ChIP-chip提供更高的解析度,較少的雜訊,較少的ChIP-DNA的量,及可偵測的動態範圍及基因體範圍較廣,因此可呈現較真實的基因調控及表觀遺傳學現況。

20120914_pic1

如何分析ChIP-Seq資料?

從次世代定序儀所得到的影像檔,會轉換成核苷酸序列,並計算每個核苷酸的錯誤率,將正確性高的序列對到基因體,找到Peak後,與對照組(通常是Input DNA)比較,利用統計學的計算此Enriched region的錯誤率,之後可進行其它的分析。

20120914_pic2

如何找到Protein binding site?

DNA是雙股的結構,因此ChIP-Seq是從DNA5’端定序,會對到基因體的正反股,如下圖可看到藍色序列對到的是正股,紅色序列對到的是反股,因序列的數量畫出常態分佈後找到Peak,而兩者高峰處之間為Protein b[……]

Read more

Mapping reads with bwa and bowtie

In this tutorial, we’re going to take a set of Illumina reads from an inbred Drosophila melanogaster line, and map them back to the reference genome. (After these steps, we could do things like generate a list of SNPs at which this line differs from the reference strain, or generate a genome sequence for this fly strain, but we’ll get to that later on in the course.) We are also going to use two different (but popular) mapping tools, bwa and bowtie. Among their differences is that bowtie (while smokin’ fast) does not deal with “gapped” alignments, i.e. it does not handle insertion/deletions we[……]

Read more