Remove search.4jyj.com malicious extension from Firefox and Chrome

4jyj is a malicious extension in Firefox and Chrome browser that hijacks the search engine, and redirects to yahoo.com when you search in Google. It also modifies your default search engine setting to something like DominantPartition. It cannot be removed by simply uninstalling any extension.

The following script shows how to find files 

The string with numbers can be different on different systems. 

cd ~/Library/Application\ Support/
sudo find  . -name "*DominantPartition*"
sudo rm -rf ./.1047632777245170349/Services/com.DominantPartition.service ./.1047632777245170349/Services/com.DominantPartition.service/DominantPartition.service
 

cd ~/Library/LaunchAgents/
rm -rf com.DominantPartition.service.plist
cd /Library/Application\ Support/
sudo find  . -name "*DominantPartition*"
sudo rm -rf ./.1047632777245170349/System/com.DominantPartition.system ./.1047632777245170349/System/com.DominantPartition.system/DominantPartition.system

cd /Library/LaunchDaemons/
sudo rm -rf com.DominantPartition*

Short comments on Nature's new open-access initiative

Nature announces a new open-initiative that allows any subscriber individuals and institutions to share a public-access link to Nature articles (http://www.nature.com/press_releases/share-nature-content.html).

It is a good, but small, step forward, although the intention of Nature is still to maximize their revenues from subscription business.

I would also encourage all authors to "self-archiving" their papers / manuscripts on their personal or institutional websites, which will make their work widely available and promote open access. The six-month limitation of Nature only applies to the finalized version, and it is legal to self archive the original manuscripts any time at the authors' wish (http://www.eprints.org/openaccess/self-faq/#self-archiving-legal).

Using UCSC Genome Browser Sessions to Organize and View MethBase Tracks

I would like to share a tip that uses the UCSC Genome Browser Sessions to organize and view MethBase tracks.

As of 03/18/2014, MethBase contains 2294 tracks, including tracks for methylation levels, read depths, HMRs, etc. For human alone, there are 1130 tracks. Since each one may be interested in different set of samples and different selection of features, and want to display them with specific settings, there is no single setting that satisfies everyone. Fortunately, the UCSC Genome Browser Sessions provides a nice feature for you to 1) manually tailor a set of tracks (e.g., brain related), 2) store them for future re-use, and 3) share them with others. With UCSC Genome Browser, each one will have a "personalized" view of MethBase.

Using the Session feature requires you to create an account with UCSC Genome Browser and is quite straightforward (http://genome.ucsc.edu/goldenPath/help/hgSessionHelp.html). For example, here is a UCSC browser session that contains high coverage methylomes of normal cell samples from human (link).

Hopefully, more interesting and useful sessions, such as brain development oriented and cancer oriented, will be created and shared.

A Survey of Commercial Providers of Whole Genome Bisulfite Sequencing Services

These days I am working on an NIH proposal that involves whole genome bisulfite sequencing (WGBS). For the purpose of project budgeting, I surveyed several commercial providers of WGBS. The following list of companies provide WGBS service, including library preparation and high through sequence with Illumina machines. Most of them also provide additional bioinformatics service as well. Our project only requires library preparation and sequencing services, the quote from the lowest to highest are: BGI < SeqWright < NXT-dx < Glocal Biologics < Alpha Biolaboratory < ACGT. Illumia only offers service if the desired coverage of a single sample is above 30x, and I have not heared back
from Zymo Research yet.

• BGI Americas http://bgiamericas.com
• Zymo Research http://www.zymoresearch.com/
• SeqWright http://www.seqwright.com/
• NXT-dx http://www.nxt-dx.com/
• Illumina http://www.illumina.com/index-c.ilmn
• Alpha Biolaboratory http://alphabiolab.com/
• Global Biologics http://www.globalbiologics.us/
• ACGT Inc http://www.acgtinc.com/

A Reference Methylome Database and Analysis Pipeline to Facilitate Integrative and Comparative Epigenomics

Original link: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081148
 
DNA methylation is implicated in a surprising diversity of regulatory, evolutionary processes and diseases in eukaryotes. The introduction of whole-genome bisulfite sequencing has enabled the study of DNA methylation at a single-base resolution, revealing many new aspects of DNA methylation and highlighting the usefulness of methylome data in understanding a variety of genomic phenomena. As the number of publicly available whole-genome bisulfite sequencing studies reaches into the hundreds, reliable and convenient tools for comparing and analyzing methylomes become increasingly important. We present MethPipe, a pipeline for both low and high-level methylome analysis, and MethBase, an accompanying database of annotated methylomes from the public domain. Together these resources enable researchers to extract interesting features from methylomes and compare them with those identified in public methylomes in our database.

Examples of high-level methylation features available in MethBase through the UCSC Genome Browser track hub.

Convert PDF files to high quality PNG figures

To display figures on your website, it is necessary to convert PDF files to image files in PNG format. However, the conversion sometimes results in low-quality figures, especially if there are texts in the PDF original files. Below are the procedures I used to convert PDF files to high-quality PNG files. It includes two step:

1. use Preview to convert PDF files to PNG files
Open your pdf file with Preview on Mac OS. Click File->Export. Select PNG from the Format field. Below the Format selector, there is a text box Resolution, which is the key to preserve high quality. Make sure to input quite high number, say 300 pixel/inch. Click Save. This produces a png file of high quality

2. use OptiPNG to reduce PNG file size
The PNG file from the above step is usually quite big, which may make your website slow to load. The OptiPNG (http://optipng.sourceforge.net/) program can used to reduce file size. With the default settings, it is able to reduce the png file size by half without perceptible loss in image quality.

You may see a PNG figure produced with the above procedure in my MethPipe website (http://smithlab.usc.edu/methpipe/).

Add trunk/tags/branches directories to an existing SVN repository

In a standard SVN repository, the top level directory is the project directory, which contains three subdirectories: trunk, tags and branches (SVN Best Practices). Most time, you actively work and update the trunk directory. When you release a new version, you may take a snapshot of the trunk directory by copying the trunk directory to tags. The branches directory is where you may try out some new ideas.

Occasionally, you may have a svn repository that does not follow the recommend layout, probably because it seemed not worth the efforts when you first start that toy-like project. However as developments continue, that repository may have lots of commits, and you found it much convenient if there are the trunk/tags/branches layout (for example, link). Here I will give a step-by-step tutorial.

First, we need to dump the old repository with svnadmin, and then create a new clean repository.

svnadmin dump /srv/svn/repos/test > test-repo.dump
mv /srv/svn/repos/test /srv/svn/repos/test-backup
svnadmin create /srv/svn/repos/test

Next, check out the clean repository, and add trunk, tags, and branches directories.

svn checkout PATH-TO-TEST-REPO
cd test
svn mkdir trunk tags branches
svn ci  trunk tags branches -m "add trunk tags branches structure"

Finally, load the previous repository dump into the trunk subdirectory. Note, the --parent-dir is essential.

svnadmin load  /srv/svn/repos/test --parent-dir  trunk < test-repo.dump

Done!

A Simple Python ConfigParser Class for Parsing Configuration Files

The default ConfigParser in Python is flexible and sophisticated, but surprisingly it behaves annoyingly when working with simple configutation files. It requires that every option must belong to certain sections (link). If there is no section, it aborts with an error. Additionally, it automatically converts keys to lower case,  therefore it is case-insensitive regarding keys (link).

To deal with these annoyances, I implemented an alternative ConfigParser (https://github.com/songqiang/configparser). It aims to work simple configuration files, that contains a key and its value in each line. The delimiter between a ket and its value can be equal (=), colon (:), whitespaces and tabs. Section names are  optional. It implements the same set of interfaces of the default ConfigParser excluding the functionality for writing and sophisticated customization. To use my ConfigParser, just download the ConfigParser.py  file and put it in the same directory with the calling python script. Since Python first looks up the current working directory when importing a module, my ConfigParser will override the default one.

FASTQ Quality Score Convesion Table

In FASTQ format, the fourth line encodes the quality score of sequences in the second line. This scheme was initially used by the Phred base-calling program to use ASCII characters to encode the probability that the corresponding base call is wrong in traditional Sanger sequencing. The same format is also used by Illumina/Solexa sequencing, however the mapping from probability values to characters is slightly changed from the Phred score and also varies between different version of Solexa sequencer. The exact formula is given somewhere else. The following lists the conversion table for each platform and/or version. 

Range

  SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.....................................................
  ..........................XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX......................
  ...............................IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII......................
  .................................JJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ......................
  LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL....................................................
  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~
  |                         |    |        |                              |                     |
 33                        59   64       73                            104                   126

 S - Sanger        Phred+33,  raw reads typically (0, 40)
 X - Solexa        Solexa+64, raw reads typically (-5, 40)
 I - Illumina 1.3+ Phred+64,  raw reads typically (0, 40)
 J - Illumina 1.5+ Phred+64,  raw reads typically (3, 40)
    with 0=unused, 1=unused, 2=Read Segment Quality Control Indicator (bold) 
    (Note: See discussion above).
 L - Illumina 1.8+ Phred+33,  raw reads typically (0, 41)

Sanger sequencing score

   |------+-------+-------+--------------|
   | char | value | Phred |  Error-Prob. |
   |------+-------+-------+--------------|
   | !    |    33 |     0 | 1.0000000000 |
   | "    |    34 |     1 | 0.7943282347 |
   | #    |    35 |     2 | 0.6309573445 |
   | $    |    36 |     3 | 0.5011872336 |
   | %    |    37 |     4 | 0.3981071706 |
   | &    |    38 |     5 | 0.3162277660 |
   | '    |    39 |     6 | 0.2511886432 |
   | (    |    40 |     7 | 0.1995262315 |
   | )    |    41 |     8 | 0.1584893192 |
   | *    |    42 |     9 | 0.1258925412 |
   | +    |    43 |    10 | 0.1000000000 |
   | ,    |    44 |    11 | 0.0794328235 |
   | -    |    45 |    12 | 0.0630957344 |
   | .    |    46 |    13 | 0.0501187234 |
   | /    |    47 |    14 | 0.0398107171 |
   | 0    |    48 |    15 | 0.0316227766 |
   | 1    |    49 |    16 | 0.0251188643 |
   | 2    |    50 |    17 | 0.0199526231 |
   | 3    |    51 |    18 | 0.0158489319 |
   | 4    |    52 |    19 | 0.0125892541 |
   | 5    |    53 |    20 | 0.0100000000 |
   | 6    |    54 |    21 | 0.0079432823 |
   | 7    |    55 |    22 | 0.0063095734 |
   | 8    |    56 |    23 | 0.0050118723 |
   | 9    |    57 |    24 | 0.0039810717 |
   | :    |    58 |    25 | 0.0031622777 |
   | ;    |    59 |    26 | 0.0025118864 |
   | <    |    60 |    27 | 0.0019952623 |
   | =    |    61 |    28 | 0.0015848932 |
   | >    |    62 |    29 | 0.0012589254 |
   | ?    |    63 |    30 | 0.0010000000 |
   | @    |    64 |    31 | 0.0007943282 |
   | A    |    65 |    32 | 0.0006309573 |
   | B    |    66 |    33 | 0.0005011872 |
   | C    |    67 |    34 | 0.0003981072 |
   | D    |    68 |    35 | 0.0003162278 |
   | E    |    69 |    36 | 0.0002511886 |
   | F    |    70 |    37 | 0.0001995262 |
   | G    |    71 |    38 | 0.0001584893 |
   | H    |    72 |    39 | 0.0001258925 |
   | I    |    73 |    40 | 0.0001000000 |
   |------+-------+-------+--------------|

Solexa score (prior 1.3)

   |------+-------+-------+--------------|
   | char | value | Phred |  Error-Prob. |
   |------+-------+-------+--------------|
   | ;    |    59 |    -5 | 0.7597469266 |
   | <    |    60 |    -4 | 0.7152527510 |
   | =    |    61 |    -3 | 0.6661394246 |
   | >    |    62 |    -2 | 0.6131368202 |
   | ?    |    63 |    -1 | 0.5573116338 |
   | @    |    64 |     0 | 0.5000000000 |
   | A    |    65 |     1 | 0.4426883662 |
   | B    |    66 |     2 | 0.3868631798 |
   | C    |    67 |     3 | 0.3338605754 |
   | D    |    68 |     4 | 0.2847472490 |
   | E    |    69 |     5 | 0.2402530734 |
   | F    |    70 |     6 | 0.2007600089 |
   | G    |    71 |     7 | 0.1663375308 |
   | H    |    72 |     8 | 0.1368068886 |
   | I    |    73 |     9 | 0.1118157698 |
   | J    |    74 |    10 | 0.0909090909 |
   | K    |    75 |    11 | 0.0735875561 |
   | L    |    76 |    12 | 0.0593509431 |
   | M    |    77 |    13 | 0.0477267210 |
   | N    |    78 |    14 | 0.0382865039 |
   | O    |    79 |    15 | 0.0306534300 |
   | P    |    80 |    16 | 0.0245033676 |
   | Q    |    81 |    17 | 0.0195623039 |
   | R    |    82 |    18 | 0.0156016622 |
   | S    |    83 |    19 | 0.0124327353 |
   | T    |    84 |    20 | 0.0099009901 |
   | U    |    85 |    21 | 0.0078806839 |
   | V    |    86 |    22 | 0.0062700123 |
   | W    |    87 |    23 | 0.0049868787 |
   | X    |    88 |    24 | 0.0039652856 |
   | Y    |    89 |    25 | 0.0031523092 |
   | Z    |    90 |    26 | 0.0025055927 |
   | [    |    91 |    27 | 0.0019912892 |
   | \\   |    92 |    28 | 0.0015823853 |
   | ]    |    93 |    29 | 0.0012573425 |
   | ^    |    94 |    30 | 0.0009990010 |
   | _    |    95 |    31 | 0.0007936978 |
   | `    |    96 |    32 | 0.0006305595 |
   | a    |    97 |    33 | 0.0005009362 |
   | b    |    98 |    34 | 0.0003979487 |
   | c    |    99 |    35 | 0.0003161278 |
   | d    |   100 |    36 | 0.0002511256 |
   | e    |   101 |    37 | 0.0001994864 |
   | f    |   102 |    38 | 0.0001584642 |
   | g    |   103 |    39 | 0.0001258767 |
   | h    |   104 |    40 | 0.0000999900 |
   |------+-------+-------+--------------|

Solexa score 1.3+

   |------+-------+-------+--------------|
   | char | value | Phred |   Error Prob |
   |------+-------+-------+--------------|
   | @    |    64 |     0 | 1.0000000000 |
   | A    |    65 |     1 | 0.7943282347 |
   | B    |    66 |     2 | 0.6309573445 |
   | C    |    67 |     3 | 0.5011872336 |
   | D    |    68 |     4 | 0.3981071706 |
   | E    |    69 |     5 | 0.3162277660 |
   | F    |    70 |     6 | 0.2511886432 |
   | G    |    71 |     7 | 0.1995262315 |
   | H    |    72 |     8 | 0.1584893192 |
   | I    |    73 |     9 | 0.1258925412 |
   | J    |    74 |    10 | 0.1000000000 |
   | K    |    75 |    11 | 0.0794328235 |
   | L    |    76 |    12 | 0.0630957344 |
   | M    |    77 |    13 | 0.0501187234 |
   | N    |    78 |    14 | 0.0398107171 |
   | O    |    79 |    15 | 0.0316227766 |
   | P    |    80 |    16 | 0.0251188643 |
   | Q    |    81 |    17 | 0.0199526231 |
   | R    |    82 |    18 | 0.0158489319 |
   | S    |    83 |    19 | 0.0125892541 |
   | T    |    84 |    20 | 0.0100000000 |
   | U    |    85 |    21 | 0.0079432823 |
   | V    |    86 |    22 | 0.0063095734 |
   | W    |    87 |    23 | 0.0050118723 |
   | X    |    88 |    24 | 0.0039810717 |
   | Y    |    89 |    25 | 0.0031622777 |
   | Z    |    90 |    26 | 0.0025118864 |
   | [    |    91 |    27 | 0.0019952623 |
   | \\   |    92 |    28 | 0.0015848932 |
   | ]    |    93 |    29 | 0.0012589254 |
   | ^    |    94 |    30 | 0.0010000000 |
   | _    |    95 |    31 | 0.0007943282 |
   | `    |    96 |    32 | 0.0006309573 |
   | a    |    97 |    33 | 0.0005011872 |
   | b    |    98 |    34 | 0.0003981072 |
   | c    |    99 |    35 | 0.0003162278 |
   | d    |   100 |    36 | 0.0002511886 |
   | e    |   101 |    37 | 0.0001995262 |
   | f    |   102 |    38 | 0.0001584893 |
   | g    |   103 |    39 | 0.0001258925 |
   | h    |   104 |    40 | 0.0001000000 |
   |------+-------+-------+--------------|

Solexa score 1.5+

   |------+-------+-------+--------------|
   | char | value | Phred |   Error Prob |
   |------+-------+-------+--------------|
   | C    |    67 |     3 | 0.5011872336 |
   | D    |    68 |     4 | 0.3981071706 |
   | E    |    69 |     5 | 0.3162277660 |
   | F    |    70 |     6 | 0.2511886432 |
   | G    |    71 |     7 | 0.1995262315 |
   | H    |    72 |     8 | 0.1584893192 |
   | I    |    73 |     9 | 0.1258925412 |
   | J    |    74 |    10 | 0.1000000000 |
   | K    |    75 |    11 | 0.0794328235 |
   | L    |    76 |    12 | 0.0630957344 |
   | M    |    77 |    13 | 0.0501187234 |
   | N    |    78 |    14 | 0.0398107171 |
   | O    |    79 |    15 | 0.0316227766 |
   | P    |    80 |    16 | 0.0251188643 |
   | Q    |    81 |    17 | 0.0199526231 |
   | R    |    82 |    18 | 0.0158489319 |
   | S    |    83 |    19 | 0.0125892541 |
   | T    |    84 |    20 | 0.0100000000 |
   | U    |    85 |    21 | 0.0079432823 |
   | V    |    86 |    22 | 0.0063095734 |
   | W    |    87 |    23 | 0.0050118723 |
   | X    |    88 |    24 | 0.0039810717 |
   | Y    |    89 |    25 | 0.0031622777 |
   | Z    |    90 |    26 | 0.0025118864 |
   | [    |    91 |    27 | 0.0019952623 |
   | \\   |    92 |    28 | 0.0015848932 |
   | ]    |    93 |    29 | 0.0012589254 |
   | ^    |    94 |    30 | 0.0010000000 |
   | _    |    95 |    31 | 0.0007943282 |
   | `    |    96 |    32 | 0.0006309573 |
   | a    |    97 |    33 | 0.0005011872 |
   | b    |    98 |    34 | 0.0003981072 |
   | c    |    99 |    35 | 0.0003162278 |
   | d    |   100 |    36 | 0.0002511886 |
   | e    |   101 |    37 | 0.0001995262 |
   | f    |   102 |    38 | 0.0001584893 |
   | g    |   103 |    39 | 0.0001258925 |
   | h    |   104 |    40 | 0.0001000000 |
   |------+-------+-------+--------------|

Solexa score 1.8+

   |------+-------+-------+--------------|
   | char | value | Phred |  Error-Prob. |
   |------+-------+-------+--------------|
   | !    |    33 |     0 | 1.000000e+00 |
   | "    |    34 |     1 | 7.943282e-01 |
   | #    |    35 |     2 | 6.309573e-01 |
   | $    |    36 |     3 | 5.011872e-01 |
   | %    |    37 |     4 | 3.981072e-01 |
   | &    |    38 |     5 | 3.162278e-01 |
   | '    |    39 |     6 | 2.511886e-01 |
   | (    |    40 |     7 | 1.995262e-01 |
   | )    |    41 |     8 | 1.584893e-01 |
   | *    |    42 |     9 | 1.258925e-01 |
   | +    |    43 |    10 | 1.000000e-01 |
   | ,    |    44 |    11 | 7.943282e-02 |
   | -    |    45 |    12 | 6.309573e-02 |
   | .    |    46 |    13 | 5.011872e-02 |
   | /    |    47 |    14 | 3.981072e-02 |
   | 0    |    48 |    15 | 3.162278e-02 |
   | 1    |    49 |    16 | 2.511886e-02 |
   | 2    |    50 |    17 | 1.995262e-02 |
   | 3    |    51 |    18 | 1.584893e-02 |
   | 4    |    52 |    19 | 1.258925e-02 |
   | 5    |    53 |    20 | 1.000000e-02 |
   | 6    |    54 |    21 | 7.943282e-03 |
   | 7    |    55 |    22 | 6.309573e-03 |
   | 8    |    56 |    23 | 5.011872e-03 |
   | 9    |    57 |    24 | 3.981072e-03 |
   | :    |    58 |    25 | 3.162278e-03 |
   | ;    |    59 |    26 | 2.511886e-03 |
   | <    |    60 |    27 | 1.995262e-03 |
   | =    |    61 |    28 | 1.584893e-03 |
   | >    |    62 |    29 | 1.258925e-03 |
   | ?    |    63 |    30 | 1.000000e-03 |
   | @    |    64 |    31 | 7.943282e-04 |
   | A    |    65 |    32 | 6.309573e-04 |
   | B    |    66 |    33 | 5.011872e-04 |
   | C    |    67 |    34 | 3.981072e-04 |
   | D    |    68 |    35 | 3.162278e-04 |
   | E    |    69 |    36 | 2.511886e-04 |
   | F    |    70 |    37 | 1.995262e-04 |
   | G    |    71 |    38 | 1.584893e-04 |
   | H    |    72 |    39 | 1.258925e-04 |
   | I    |    73 |    40 | 1.000000e-04 |
   | J    |    74 |    41 | 7.943282e-05 |
   |------+-------+-------+--------------|

A Guess on the Encryption Design of MEGA

The newly relaunched MEGA, successor to MegaUpload, raised lots of fanfare on the net. A novel feature of the new MEGA site is its encryption function. There are two interesting articles about the encryption technique in the new MEGA site. One from Ars Technica questioned the security and usefulness of MEGA encryption design (http://arstechnica.com/business/2013/01/megabad-a-quick-look-at-the-state-of-megas-encryption/). The other posted by MEGA blog address those concerns (https://mega.co.nz/#blog_3).

 In my opinion, the editor of Ars Technica does not understand or at least misunderstands MEGA's encryption design. There are some comments of that Ars article that explained the basic idea quite clearly, which was confirmed by Mega's reply.

 If my guess is right, the Encryption Design of MEGA is illustrated in the figure below. A pdf version of the figure is at https://www.box.com/s/uswje6orhhqahyv97ijk

Notes on Android Phones

Some notes when using my Google Nexus 4 phone.

1. backup Android files

Install Software Data Cale https://play.google.com/store/apps/details?id=com.lyy.softdatacable&hl=en. This app start a ftp server on your phone so that you can access your files with WiFi. After you start the app, it shows its IP address in the home screen, something like ftp://198.168.1.27:8888/

Next in your local machine, you can use lftp to mirror the files in your phone to your desktop machine. The following command will sync all newer files with in the root directory of your phone to the the n4 directory at your desktop

lftp ftp://198.168.1.27:8888/ -e "mirror --verbose --only-newer / n4"

2. Access Developer Options

 Go to Settings -> About Phone -> Click Build Number for seven time, the Developer option is enabled, from which you may adjust the scale of animation.

Notes on Upgrading to Ubuntu 12.04 Precise Pangolin

0. In general, the upgrading process was smooth. I have been using it for two days. Since this version is LTS, I would recommend that everyone upgrade.

1. The full-fledged Unity is so unstable that is essentially unusable. By default, the Unity Plugin is not enabled. When you log in for the first time, there is just an blank desktop, no Dash, no launcher. Some one wrote about how to fix this (http://askubuntu.com/questions/17381/unity-doesnt-load-no-launcher-no-dash-appears and http://askubuntu.com/questions/121782/blank-desktop-after-updates-today-only-unity2d-works-now), however it still does not work fine with me. In particular, if you log out and then log in again, the same blank desktop appears :-(.

2. Unity 2D works fine. The new HUD (Heads Up Display) is the killer feature.

3. Gnome Shell generally works. However the most extensions, such as User Themes, are unavailable now.

4. The default setting for dual monitors in Gnome Shell has a wired behavior. You can only switch workspaces in the primary monitor while the workspace in the secondary monitor keeps the same. To make the workspace span the two monitors, run the following command:
"gsettings set org.gnome.shell.overrides workspaces-only-on-primary false"
as pointed by http://gregcor.com/2011/05/07/fix-dual-monitors-in-gnome-3-aka-my-workspaces-are-broken/

5. In Gnome Shell, the wallpaper does not show up even I have already set it from System Settings -> Appearance. This can be fixed as following:

Open gnome-tweak-tool
click on the Desktop tab
Turn on "Have manager handle the desktop"Turn on "Computer icon visible on desktop"

Multithreaded downloading with wget

GNU wget is versatile and robust, but lacks support for multithreaded downloading. When downloading multiple files, it just goes one by one, which is quite inefficient if the bandwidth of each connection is limited.

There is a way to achieve nearly the same effect as multithreaded downloading (link),  and here is how you do it:

wget -r -np -N [url] &
wget -r -np -N [url] &
wget -r -np -N [url] &
wget -r -np -N [url] &

copy as many times as you deem appropriate to have as many processes downloading. The key is the -N option, which tells wget to download a file only when its local time stamp is older than the one in the server side.

Alternatively, I wrote a wrapper, pwget (short for parallel wget), that adds multithreading to wget. The program is available from https://github.com/songqiang/pwget. It has two options --max-num-threads and --sleep. The first option --max-num-threads gives the maximum number of connections you allow to establish. This number is usually determined by the setting on the server side and by default it is 3.  The second option --sleep specifies how often (in seconds) the master thread checks the status of downloading threads. When the master thread wakes up, it removes finished threads and add new downloading threads if necessary. Suppose you have the list of URLs in the file url-list.txt, then run

./pwget.py --max-num-threads 5 --sleep 2 -i url-list.txt

wget will begin downloading the list of URLs in url-list.txt with at most 5 connections at once. You can also specify the option for wget in the command line, which will be passed to working threads.

This tool has several limitations. The parallel level of pwget is based on each URL, so you need to list the all URLs in prior. Furthermore, if you have a single large file, pwget does not help. In that case, you may consider use aria2 (http://aria2.sourceforge.net/).   

Runnning SSH on a non-standard port

The default port for SSH connection is 22. However some servers change the default port to others, for example 22222, for security reasons. Here I list some common commands to deal with non-standard SSH port.

Suppose you have a SSH server ssh.example.edu with ssh port number 22222.

To copy your ssh public key to the server, run:

ssh-copy-id '-p 22222 jon@ssh.example.edu'

Note the single quote is necessary.

To log in to the SSH server, run

ssh -p 22222 jon@ssh.example.edu 

 To copy files between your local machine and the server with scp, run

scp -P 22222 local-files jon@ssh.example.edu:~

 Note the "-P" option is capitalised.

References:
1. http://www.itworld.com/nls_unixssh0500506
2. http://mikegerwitz.com/2009/10/07/ssh-copy-id-and-sshd-port/

Setting Up a Hadoop Cluster

This post lists the steps to set up an Hadoop cluster in Ubuntu 11.04. Most codes can be directly copied and pasted.

* Hadoop
** Install Java

#+begin_src shell
sudo apt-get install sun-java6-jdk
sudo update-java-alternatives -s java-6-sun
#+end_src

** Add Hadoop User and Group

#+begin_src shell
sudo addgroup hadoop
sudo adduser --ingroup hadoop hadoop
#+end_src

** Configuring SSH and Password-less Login

#+begin_src sh
  # In the master node
  su hadoop
  ssh-keygen -t rsa -P ""
 
  for node in $(cat /conf/slaves);
  do
      ssh-copy-id -i $HOME/.ssh/id_rsa.pub hadoop@$node;
  done
#+end_src

** Install Hadoop
*** Install

#+begin_src sh
  ## download and install
  cd /home/hadoop/
  tar xzf hadoop-0.21.0.tar.gz
  mv hadoop-0.21.0 hadoop
#+end_src

*** Update .bashrc

#+begin_src sh
  ## update .bashrc
  # Set Hadoop-related environment variables
  export HADOOP_HOME=/home/hadoop/hadoop
  export HADOOP_COMMON_HOME="/home/hadoop/hadoop"
  export PATH=$PATH:$HADOOP_HOME/bin
  export PATH=$PATH:$HADOOP_COMMON_HOME/bin/
#+end_src

*** Update conf/hadoop-env.sh

#+begin_src sh
  export JAVA_HOME=/usr/lib/jvm/java-6-sun
  export HADOOP_OPTS=-Djava.net.preferIPv4Stack=true
#+end_src

*** Update conf/core-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<configuration>

<!-- In: conf/core-site.xml -->
<property>
<name>hadoop.tmp.dir</name>
<value>/home/hadoop/tmp</value>
<description>A base for other temporary directories.</description>
</property>

<property>
<name>fs.default.name</name>
<value>hdfs://128.125.86.89:54310</value>
<description>The name of the default file system. A URI whose
scheme and authority determine the FileSystem implementation. The
uri's scheme determines the config property (fs.SCHEME.impl) naming
the FileSystem implementation class. The uri's authority is used to
determine the host, port, etc. for a filesystem.</description>
</property>


</configuration>

*** Update conf/mapred-site.xml

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Put site-specific property overrides in this file. -->

<configuration>

<!-- In: conf/mapred-site.xml -->
<property>
<name>mapreduce.jobtracker.address</name>
<value>128.125.86.89:54311</value>
</property>

</configuration>

*** Update conf/hdfs-site.xml

#+begin_src html
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Put site-specific property overrides in this file. -->

<configuration>

<!-- In: conf/hdfs-site.xml -->
<property>
<name>dfs.replication</name>
<value>3</value>
<description>Default block replication.
The actual number of replications can be specified when the file is created.
The default is used if replication is not specified in create time.
</description>
</property>

</configuration>
#+end_src

*** Update conf/masters (master node only)

#+begin_src sh
128.125.86.89
#+end_src

*** Update conf/slaves (master node only)

#+begin_src sh
128.125.86.89
slave-ip1
slave-ip2
......
#+end_src

*** Copy hadoop installation and configuration files to slave nodes

#+begin_src sh  
# In the master node  
su hadoop    
for node in $(cat /conf/slaves);  
do
      scp ~/.bashrc hadoop@$node:~;       scp -r ~/hadoop hadoop@#node:~;  
done
#+end_src

** Run Hadoop
*** Format HDFS

#+begin_src sh
hdfs namenode -format
#+end_src

*** Start Hadoop

#+begin_src sh
start-dfs.sh && sleep 300 && start-mapred.sh && echo "GOOD"
#+end_src

*** Run Jobs

#+begin_src sh
hadoop jar hadoop pipes
#+end_src

*** Stop Hadoop

#+begin_src sh
stop-mapred.sh && stop-dfs.sh
#+end_src

** References:
1. http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-single-node-cluster/ 
2. http://www.michael-noll.com/tutorials/running-hadoop-on-ubuntu-linux-multi-node-cluster/
3. http://fclose.com/b/cloud-computing/290/hadoop-tutorial/
4. Fix could only be replicated to 0 nodes instead of 1 error

Identifying dispersed epigenomic domains from ChIP-Seq data

Published in Bioinformatics http://bioinformatics.oxfordjournals.org/content/27/6/870.full

1 INTRODUCTION

Post-translational modifications to histone tails, including methylation and acetylaytion, have been associated with important regulatory roles in cell differentiation and disease development (Kouzarides, 2007). The application of ChIP-Seq to histone modification study has proved very useful for understanding the genomic landscape of histone modifications (Barski et al., 2007; Mikkelsen et al., 2007). Certain histone modifications are tightly concentrated, covering a few hundred base pairs. For example, H3K4me3 is usually associated with active promoters, and occurs only at nucleosomes close to transcription start sites (TSSs). On the other hand, many histone modifications are diffuse and occupy large regions, ranging from thousands to several millions of base pairs. A well known example H3K36me3 is associated with active gene expression and often spans the whole gene body (Barski et al., 2007). Reflected in ChIP-Seq data, the signals of these histone modifications are enriched over large regions, but lack well-defined peaks. It is worth pointing out that the property of being ‘diffuse’ is matter of degrees. Besides the modification frequency, the modification profile over a region is also affected by nucleosome densities and the strength of nucleosome positioning. By visual inspection of read-density profiles, we found that H2BK5me1, H3K79me1, H3K79me2, H3K79me3, H3K9me1, H3K9me3 and H3R2me1 show similar diffuse profiles.

There are several general questions about dispersed epigenomic domains that remain unanswered. Many of these questions center around how these domains are established and maintained. One critical step in answering these questions is to accurately locate the boundaries of these domains. However, most of existing methods for ChIP-Seq data analysis were originally designed for identifying transcription factor binding sites. These focus on locating highly concentrated ‘peaks’, and are inappropriate for identifying domains of dispersed histone modification marks (Pepke et al., 2009). Moreover, the quality of ‘peak’ analysis is measured in terms of sensitivity and specificity of peak calling (accuracy), along with how narrow the peaks are (precision; often determined by the underlying platform). But for diffuse histone modifications, significant ‘peaks’ are usually lacking and often the utility of identifying domains depends on how clearly the boundaries are located.

Previous SectionNext Section

2 METHODS

Our method for identifying epigenomic domains is based on hidden Markov model (HMM) framework including the Baum–Welch training and posterior decoding (see Rabiner, 1989 for a general description).

Single sample analysis: we first obtain the read density profile by dividing the genome into non-overlapping fixed length bins and counting the number of reads in each bin. The bin size can be determined automatically as a function of the total number of reads and the effective genome size (Supplementary Section S1.5). We model the read counts with the negative binomial distribution after correcting for the effect of genomic deadzones. We first exclude unassembled regions of a genome from our analysis. Second, when two locations in the genome have identical sequences of length greater than or equal to the read length, any read derived from one of those locations will necessarily be ambiguous and is discarded. We refer to contiguous sets of locations to which no read can map uniquely as ‘deadzones’. Those bins within large deadzones (referred to as ‘deserts’) are ignored. For those bins outside of deserts, we correct for the deadzone effect by scaling distribution parameters according to the proportion of the bin which is not within a deadzone (Supplementary Section S1.3).

We assume a bin may have one of the two states: foreground state with high histone modification frequency and background state with low histone modification frequency. We developed a two state HMM for segmentation the genome into foreground domains and background domains.

Identifying and evaluating domain boundaries: while predicted domains themselves give the locations of boundaries, we characterize the boundaries with the following metrics. We evaluate domain boundaries based on posterior probabilities of transitions between the foreground state and the background state as estimated by the HMM. For each pair of consecutive genomic bins, the posterior probability is calculated for all possible transitions between those bins. If a boundary corresponds to the beginning of a domain, the boundary score is the posterior probability of a background to foreground transition and vice versa.

Next an empirical distribution of posterior transition probabilities is constructed by computing posterior transition probabilities from a dataset of randomly permuted bins with the same HMM parameters. Those bins whose posterior transition probabilities have significant empirical P-values are kept and consecutive significant bins are joined as being one boundary. We score each boundary with the posterior probability that a single transition occurs in this boundary. The peak of a boundary is set to the start of the bin with the largest transition probability (see Supplementary Section S3 for details).

Incorporating a control sample: ChIP-Seq experiments are influenced by background noises, contamination and other possible sources of error, and researchers have begun to realize the necessity of generating experimental controls in ChIP-Seq experiments. Two common forms of control exist: a non-specific antibody such as IgG to control the immunoprecipitation, and sequencing of whole cell extract to control for contamination and other possible sources of error. With the availability of a control sample, we use a similar two-state HMM with the novel NBDiff distribution to describe the relationship between the read counts in the two samples. Analogous to the Skellam distribution (Skellam, 1946), the NBDiff distribution describes the difference of two independent negative binomial random variables (see Supplementary Section S1.2 for details).

Simultaneously segmenting two modifications: the simultaneous analysis of two histone modification marks may reveal more accurate information about the status of genomic regions. It helps to understand the functions of different histone modification marks. It is also of interest to compare samples from different cells types because histone modification patterns are dynamic and subject to change during cell differentiation. We use the NBDiff distribution to model the read count difference between the two samples, and employ three-state HMM: where the basal state means these two signals are similar, the second state represents the signal in test sample A is greater than that in the test sample B and the third state represents the opposite case (details given in Supplementary Section S2.1).

Previous SectionNext Section

3 EVALUATION AND APPLICATIONS

We simulated H3K36me3 ChIP-Seq data and compared RSEG, SICER (Zang et al., 2009) and HPeak (Qin et al., 2010). In terms of domain identification, RSEG outperforms SICER and HPeak for single-sample analysis and yields comparable results to SICER for analysis with control samples (Supplementary Section S4.1 and 4.2). We applied RSEG to H3K36me3 ChIP-Seq dataset from (Barski et al., 2007) and found a strong association between H3K36me3 domain boundaries with TSS and transcription termination site (TTS), which supports that RSEG can find high-quality domain boundaries (Supplementary Section S4.3).

We applied RSEG to four histone modification marks (H3K9me3, H3K27me3, H3K36me3 and H3K79me2) from two separate studies (Barski et al., 2007; Mikkelsen et al., 2007) (Supplementary Section S5.1). In particular, we discovered an interesting relationship between the two gene-overlapping marks H3K36me3 and H3K79me2 through boundary analysis. H3K79me2 tends to associate with 5^′-ends of genes, while H3K36me3 associates with 3^′-ends. About 41% of gene-overlapping K79 domains cover TSS in contrast to 11% of K36 domains. On the other hand, 84% of K36 domains cover TTS in contrast to 23% of K79 domains (Table 1). In those genes with both H3K36me3 and H3K79me2 signals, H3K79me2 domains tend to precede H3K36me3 domains, for example the DPF2 gene (Fig. 1) (see Supplementary Section S5.2 for more information). This novel discovery demonstrates the usefulness of boundary analysis for dispersed histone modification marks.

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Fig. 1.

The H3K36me3 and H3K79me2 domains and their boundaries at DPF2 (chr11:64,854,646–64,880,304).

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Table 1.

Location of H3K36me3 and H3K79me2 domain boundaries relative to genes

Finally we applied our three-state HMM to simultaneously analyze H3K36me3 and H3K79me2 (Supplementary Section S5.4). The result agrees with the above observations. The application of our three-state HMM to find differentially histone modification regions is given in Supplementary Section S5.3.

Sustainable Scientific Data Archiving Model

As many researchers may have noticed, NCBI plans to discontinue the Short Read Archive (SRA) service due to budget constraints. This news surprises me, and, I believe, concerns the broad biomedical research community in general. While the biomedial research enters the -omics era and becomes more and more data-driven, the sudden close of SRA raises the question that how the scientific data be archived with a sustainable model? I discuss two strategies to preserve scientific data in a sustainable manner. The first proposes a central data repository that charges data deposition fee. The other approach proposes that the data is stored in P2P manner and a central gateway gathers metadata and tracks links to P2P seeds.

Sustainable data archiving model includes the following aspects: first the data should include necessary and accurate metadata; second, the data should be stored securely and remains authentic and correct for a long time; third the data should also include essential softwares and scripts to analyze the data; finally the data should be easily searched and accessed by the broader research community now and for a considerate period in the future so that researchers may use the dataset from different perspectives and even re-analyze the data in the future if new hypothesis and analytic methods emerges.

However as has been note elsewhere, there is a disconnection between the effort to produce the data and the effort to preserve the data. Simply put, funding agencies provide the money for produce the money but not the money to maintain the data. The grant for producing the data is in rather smaller time sale, usually two to five years. Once the grant is over, the project is done and the original researchers switched to other projects, the data produced is in the danger of being lost. Fortunately the biomedical research community has a pretty good record in depositing biological datasets for public research as has been exemplified by GeneBank and GEO. The Short Read Archive is designed to meet the requirements of the massively parallel sequencing reads data. However the discontinuance of this services demonstrates the uncertainty of current data sharing model due to lack of specific funding. Therefore I am considering the following two strategies for the sustain scientific data.

In the first strategy, we still rely on a central data repository like SRA that curates, stores and distributes biological datasets. To meet the financial requirement of such central repository, it charges certain amount of fee for the data hosted. It works as following: when the original data producer finish their research and submit a paper to a journal. The journal requires that their data is deposited in a certain repository and charges data deposition fee. Next the journal allocates the major part of the data deposition fee to the central data repository. The proposed data deposition fee is charged only once which can therefore be covered by the initial grant of the original data producer. With the ever-decreasing cost of data storage, the continual influx of single-time data deposition fee should keep the central data repository working.

The second strategy is initially brought up to me by my friend Li Xia and are further inspired by Morgan Langille, the creator of BioTorrents. In the strategy, the data set is stored by multiple hosts
who may have the resources and interest to keep the dataset. Next a central gateway keeps tracks of the BitTorrents seeds to the raw data and also stores the metadata associated with each data, such as the contact of data producer, experimental protocols and descriptions of the raw data. Especially the central gateway stores the version of the raw dataset and the MD5 or SHA sum for the data so that the data users can make sure they are obtaining updated and authentic dataset from essentially unreliable and untrustable data hosts in a P2P network. Since the central gateway needs only to track these metadata, its running cost is significantly smaller than the central data repository and therefore it can work just as a new section in the NCBI infrastructure.

I hope this discussion publicize the urgency for sustainable scientific data archiving so that the biomedical research community will work out a way after SRA ends.

Next Generation Genome Browser

When David, Fang Fang and I talk about UCSC Genome Browser today, I said "I would like a genome browser like Google Map". Later I find I become more excited about this idea: the next generation genome browser, which provides an more user friendly and powerful platform to ornanize and display genomic information.

What does the next genome browser ("genome map") look like ?

First, smoother zoom in and zoom out. Genomes are organized in hierarchical structure. Sometimes we need a birdview of the whole genome and sometimes we are interested in subtle local structures. It is of great value if we are change the resolution when examining the genome. So, we need dynamic and smoother zoom in and zoom out just like the little sliding bar in Google map. (update: I came across Jbrowser and Anno J browser that seems to have this function. See reference)

Second, advanced searching functions. Current genome browser are only able to search by genomic location, as a result the vast amount of annotation information can not be searched in genome browser. It will be cool there is a search box. Users input a keyword , such as a gene name and the our genome map display those regions match the query.

Third, what kind of web technology should we need? Ajax? Database back end? XML? Maybe google map is a good starting point.

There seems great possibility that such a genome map will appear and what other features are you looking for in the next generation genome brower?

Ref:
1. Skinner ME, Uzilov AV, Stein LD, Mungall CJ, Holmes IH. JBrowse: A next-generation genome browser. Genome Res. (2009) http://jbrowse.org/
2. AnnoJ Browser http://www.annoj.org/index.shtml

Exon/Intron Statistics in Human Genome

Data from: http://www.bioinfo.de/isb/2004/04/0032/main.html#tab-1

Table 1:

Exon - intron distributions for human genome

Chr #	Total # genes	Total # exons	Total # introns	Max # exons/gene	Chromosome size (determined)	Avg # of exons/gene	Avg length (bp)		Std dev.		Total length (bp)		Shortest (bp)			Longest (bp)
							exon	intron	exon	intron	exon	intron	exon	intron	gene	exon	intron	gene
1	2514	22345	19831	107	226828929	8.89	167.01	4736.52	229.37	14268.19	3731870	93929919	2	1	78	8449	476158	980961
2	1354	12506	11152	148	238349289	9.24	163.98	5883.23	226.88	17012.24	2050855	65609873	2	1	90	7572	483412	1897544
3	1394	13517	12123	118	195073306	9.70	164.06	6375.63	224.21	21019.22	2217700	77291760	2	1	150	6654	497816	990999
4	926	8299	7373	85	187239983	8.96	174.78	7168.94	266.64	19497.08	1450541	52856617	2	53	132	6255	494708	1467842
5	1186	9946	8760	90	177696509	8.39	189.50	7277.28	332.86	21277.20	1884777	63748970	2	1	150	6574	370360	930401
6	1306	11406	10100	145	169212327	8.73	173.62	5961.61	253.56	18967.75	1980397	60212251	2	31	159	7152	469892	1377570
7	2508	23045	20537	82	310210944	9.19	167.87	6703.87	271.88	20177.41	3868769	137677396	2	1	14	11923	458139	1641567
8	908	7823	6915	86	143297300	8.62	171.16	7354.15	258.43	21384.09	1339052	50853964	2	54	84	7308	453268	2055833
9	1033	8941	7908	72	117790386	8.66	170.66	5351.68	253.19	14121.26	1525926	42321074	2	33	105	6598	276306	865661
10	1017	10273	9256	69	132016990	10.10	153.79	6412.91	219.97	20271.48	1579898	59357955	2	52	105	7812	482575	1727184
11	1567	12459	10892	87	130908954	7.95	177.66	4341.42	237.03	15362.46	2213526	47286795	3	1	87	6183	437543	1463302
12	1299	12399	11100	89	129826379	9.55	158.07	4570.21	192.23	12979.23	1959945	50729293	2	30	81	6324	328545	1248678
13	426	3784	3358	83	95749578	8.88	183.47	7351.75	396.79	19082.4	694268	24687182	2	37	279	11555	317646	1175762
14	854	6837	6106	114	87191216	8.01	176.24	5653.70	276.66	19076.38	1204982	33826109	2	51	51	11304	479079	1210740
15	843	8106	7263	104	81992482	9.62	169.79	4660.70	271.38	11542.05	1376321	33850721	2	1	168	9527	207178	620362
16	1093	9986	8893	62	79932432	9.14	166.96	3661.25	242.60	13092.99	1667340	32559472	2	1	75	8607	466049	1167938
17	1459	13179	11720	74	79376966	9.03	165.08	3193.16	215.89	9875.72	2175698	37423835	2	30	63	4786	283762	712668
18	367	3333	2966	75	74658403	9.08	174.9	7905.40	256.53	19377.24	583054	23447419	3	67	225	4721	411175	1189866
19	1609	12169	10560	106	55878340	7.56	187.31	2032.87	279.92	4741.54	2279436	21467122	2	1	81	5059	170796	298909
20	775	6492	5717	80	59424990	8.38	160.34	4403.10	215.29	13613.39	1040952	25172558	3	54	135	3738	303713	1108855
21	309	2539	2230	47	33924367	8.22	168.59	5086.89	306.51	16098.67	428056	11343761	3	74	102	5916	323563	833627
22	671	5173	4502	54	34352072	7.71	171.14	3924.83	281.85	12999.39	885356	17669584	3	42	38	6762	447252	492969
X	1048	8568	7520	79	152118949	8.18	185.33	7627.85	299.66	23527.35	1587926	57361443	2	54	129	6102	493512	2217347
Y	98	660	562	44	24649555	6.73	173.74	5288.54	255.05	19676.46	114670	2972162	3	67	228	2493	400349	681119

Emacs Note

1. Packages for using emacs as an IDE

http://xtalk.msk.su/~ott/common/emacs/rc/emacs-rc-cedet.el.html
http://cedet.sourceforge.net/
http://cscope.sourceforge.net/
http://ecb.sourceforge.net/

2. How can I use emacs without gui when I work on a remote machine with a slow connection?

emacs -nw

3. In emacs shell mode, what setting need I modify to make the shell promote PS1 display correctly, e.g. with color like in a terminal?

Add the following code in your .emacs

(autoload 'ansi-color-for-comint-mode-on "ansi-color" nil t)
(add-hook 'shell-mode-hook 'ansi-color-for-comint-mode-on)

4. After I update to emacs 23, invoking flyspell-mode gives the following error "Enabling flyspell-mode gave an error". 


This is caused by the conflicts between site dictionaries and the dictionaries in emacs 23.It can be fixed as following:

cd /usr/share/emacs23/site-lisp/dictionaries-common
sudo rm *.el *.elc

5. How do I enable double spaces in emacs?

This feature is provided in the package setspace. Add the following commands in the preamble of your tex file.

\usepackage{setspace}
\doublespacing

6. Which font looks pretty in emacs?
My personal favourite is Nimbus Mono L regular. 

7. In org-mode, how can I change the default browser?
Add the following two lines in your .emacs file:

(setq browse-url-browser-function (quote browse-url-generic))
(setq browse-url-generic-program "google-chrome")

Similarly, if you want to use the open sourced version of Chrome Browser instead of the Google rebranded version, replace "google-chrome" with "chromium-browser"; if you want to use firefox, replace "google-chrome" with "firefox". 

8. I copy some text in emacs, how can I paste the text another application?
In your .emacs file. add the follow line

(setq x-select-enable-clipboard t)