Simple pubmed API

if ($xml = simplexml_load_file('http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmax=0&usehistory=y&term=' . urlencode($argv[1]))){
if ($xml = simplexml_load_file("http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&retmode=xml&query_key={$xml->QueryKey}&WebEnv={$xml->WebEnv}&retstart=0&retmax=10")){
$docs = $xml->DocSum;
}
}
print_r($docs);
More can be found here

Anne Wojcicki talks at TedMed - an interesting video

Packaging your source code

I have written down few software packages but never released any of them officially into the nicer ./configure && make && make install format.

We have created a C package that does what GCRMA would do plus much more in a very memory efficient way. Although GCRMA C source is available for free consumption, it needed quite some work on our end to customize it. Now that it is already written down, I am settling for releasing it under GNU open source license.

Few things need to be taken care of before creating the package:

1. Put all your C sources under src/ directory
2. All data such as sample CEL files need to go to /data directory
3. Accessory scripts like split file, merge file, plotting with R need to go to script/ directory
4. Documents go to /doc directory

RUN CONFIGURATION UTILITY:

First run autoscan

With all likelihood this command will exit with error. Nevertheless, it produces a configure.scan file.
Open configure.scan file and edit the line with
AC_INIT(PACKAGE NAME, VERSION, CONTACT EMAIL)
into useful inputs like
AC_INIT(Modified GCRMA, 1.0, tsucheta@gmail.com)

$ mv configure.scan configure.ac(Note configure.in used to be the earlier version)

$autoconf

This will create the configure file.

Makefile.am

You need to create a series of makefile.am files each inside your /script /man/ doc/ bin/ directories with appropriate values.

In your src/ directory you may like to keep useful information like:

# what flags you want to pass to the C compiler & linker
AM_CFLAGS = --pedantic -Wall -std=c99 -O2
AM_LDFLAGS =

# this lists the binaries to produce, the (non-PHONY, binary) targets in
# the previous manual Makefile


#bin_SCRIPTS = scripts/split.pl scripts/merge.pl scripts/plot.R
bin_PROGRAMS = Modified GCRMA
loglikelihood_SOURCES = file1.c file2.c file3.c file4.c file5.c...

GCRMA_LDADD = -lm -lz
main.o: main.c utility.h
cc -c main.c
calculate.o: calculate.c utility.h
cc -c calculate.c
read_seq.o: read_seq.c utility.h
cc -c read_seq.c
read_file.o: read_file.c utility.h
cc -c read_file.c
rev_complement.o: rev_complement.c utility.h
cc -c rev_complement.c
detect_chimera.o: detect_chimera.c utility.h
cc -c detect_chimera.c
find_orf.o: find_orf.c utility.h
cc -c find_orf.c
command.o: command.c utility.h
cc -c command.c
process.o: process.c utility.h
cc -c process.c
clean:
rm -f *.o

in scripts directory change makefile.am into
bin_SCRIPTS = file1.pl file2.pl file3.R file4.sh....

in Man directory change
man_MANS = man.1 man.2 man.3...

Now go back to configure.ac file and make changes to the line just after

AC_INIT()
into
AM_INIT_AUTOMAKE(ModifiedGCRMA, 1.0). This will initialize automake

And at the bottom of the configure.ac file make the following changes

AC_OUTPUT(Makefile src/Makefile doc/Makefile man/Makefile scripts/Makefile)

Now run aclocal followed by automake --add-missing

automake will read makefile.am and create a makefile.in file for configure to create a final make file.

While reading automake be cautioned - you may be asked for files
NEWS,README,AUTHORS,ChangeLog

Never mind you can create those files using
touch NEWS
touch README
touch AUTHORS
touch ChangeLog

If automake did not generate makefile.in the previous time, run it again.

you may like to build the configure script again using autoconf.

Once this is done, you are all set. You may run ./configure --prefix-path="YOUR_PATH"

followed by make make install

You may pack your stuff using command make dist.

Here few of the autoconf macros are listed.

A reasonable list of free and paid softwares for nextgen sequence analysis

http://www.oxfordjournals.org/our_journals/bioinformatics/nextgenerationsequencing.htmlA great list on free and paid softwares for nextgen sequence analysis is available at the seqanswers forum here.

To browse through a compiled list of Bioinformatics Softwares click here.

A list of publications devoted at the Bioinformatics journal here
Now I am working on samtools and bowtie and will write a detailed review soon...

AGBT 2010

AGBT(Advances in Genome Biology and Technology) was recently concluded at Florida(24-27th Feb 2010). Anthony Fejas has done a great job in putting the outlines of the presentations in his blog . Check out for details.

Aragonite - what it is?

Aragonites are Calcium carbonate crystals found in pearl.
In December 2009 issue of ProteinSpotlight, a very interesting protein is featured. This protein complex is none other than the one that gives luster to pearl. The complex is made out of three proteins, known as the Pif complex in which is found pearlin, Pif80 and Pif97. Pif80 and 97 are part of the same sequence which is subsequently cleaved into two.Pif80 – by way of its many asparagine residues – binds to calcium carbonate and not only elicits aragonite crystal formation but also has a role in the orientation of the aragonite crystals. Here is the Full link to the article..

Top five Biology papers for 2009

ScienceWatch tracks and analyzes trends in basic science research, compiles bimonthly lists of the 10 most cited papers. From that list The scientist pulled 5 papers published within the last two which were the most cited in 2009. The two topics that dominate the top five papers this year: genomics and stem cells.

Following is the list:

1. K. Takahashi, et al., "Induction of pluripotent stem cells from adult human fibroblasts by defined factors," Cell, 131: 861-72, 2007.
Citations this year: 520
Total citations to date: 886
Findings: This work from Shinya Yamanaka's lab in Japan was the first to demonstrate that induced pluripotent stem (iPS) cells can be generated from adult human dermal fibroblasts. Previous efforts by the team showed that iPS cells could be derived from mouse somatic cells. This paper was an easy top pick, receiving the most citations this year, according to ScienceWatch.

2. K.A. Frazer, et al., "A second generation human haplotype map of over 3.1 million SNPs," Nature, 449: 854-61, 2007.
Citations this year: 389
Total citations to date: 588
Findings: Since the sequencing of the human genome in 2003, the International HapMap Project has explored single nucleotide polymorphisms (SNPs) -- differences in a single letter of the DNA -- to study how these small variations affect the development of diseases and the body's response to pathogens and drugs. HapMap I, the original report, placed one SNP at roughly every 5,000 DNA letters. The newest map, featured in this paper, sequenced an additional 2 million SNPs, increasing the map's resolution to one SNP per kilobase. The additional detail allows scientists to more closely investigate patterns in SNP differences, especially in hotspot regions, or concentrated stretches of DNA.

3. A. Barski, et al., "High-resolution profiling of histone methylations in the human genome," Cell, 129: 823-37, 2007.
Citations this year: 299
Total citations to date:: 560
Findings: This study looked at how histone modifications influence gene expression in more detail than previous attempts. Using a powerful sequencing tool called Solexa 1G, the researchers mapped more than 20 million DNA sequences associated with specific forms of histones, finding there were differences in methylation patterns between stem cells and differentiated T cells.

4. E. Birney, et al., "Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project, "Nature, 447: 799-816, 2007.
Citations this year: 267
Total citations to date: 618
Findings: The ENCODE project -- ENCODE stands for the ENCyclopedia Of DNA Elements -- set out to identify all functional elements in the human genome. After examining one percent of the genome, the paper revealed several new insights about how information encoded in the DNA comes to life in a cell.

5. A M. Wernig, et al., "In vitro reprogramming of fibroblasts into a pluripotent ES-cell-like state," Nature 448: 318-24, 2007.
Citations this year: 237
Total citations to date: 512
Findings: Scientists successfully performed somatic-cell nuclear transfer (SCNT), producing stem cell lines and cloned animals for the first time using fertilized mouse eggs. This paper consistently ranked in the top 10 most cited papers in 2009, according to ScienceWatch.

Death of End Note

I guess now End Note software will die a natural death!! I am not saying this just because I am not particularly fond of this software, but lately it has fierce competitions from various resources including my NCBI. Before My NCBI came into picture, I was very fond of connotea. Connotea is very easy to link to a browser, all that you will need is to drag and drop it to the menu bar. You can create your own login and password and update all the bibliography you ever wanted. Export the bibliography as you wish.

With My NCBI, it has struck the nail right on the head of End Note. Tutorials are readily available for My NCBI for easy use. Anytime, I would prefer a browser based application than a stand alone application. End note particularly needed endless filling up forms very tedious ways. I had bad experience of interference of end note with MS office 2007. Now I will breath easy writing a manuscript.

This years Nobel Prize in Medicine

This years Nobel prize in Medicine goes jointly to Elizabeth Blackburn, Carol Greider, and Jack Szostak for their work on telomeres. In the 1970s, Blackburn identified repeating segments at the ends of DNA in Tetrahymena while Szostak found that single-stranded DNA was rapidly degraded in yeast. Blackburn and Szostak then collaborated on a project, finding that the Tetrahymena DNA protected the single-stranded DNA from degradation in yeast. In 1984, Blackburn and Greider, her graduate student, discovered telomerase. The Nobel citation lauds these researchers for their contribution to the study of aging, cancer, and other diseases. "The discoveries by Blackburn, Greider and Szostak have added a new dimension to our understanding of the cell, shed light on disease mechanisms, and stimulated the development of potential new therapies," it says.

The mysterious telomere

The chromosomes contain our genome in their DNA molecules. As early as the 1930s, Hermann Muller (Nobel Prize 1946) and Barbara McClintock (Nobel Prize 1983) had observed that the structures at the ends of the chromosomes, the so-called telomeres, seemed to prevent the chromosomes from attaching to each other. They suspected that the telomeres could have a protective role, but how they operate remained an enigma.

When scientists began to understand how genes are copied, in the 1950s, another problem presented itself. When a cell is about to divide, the DNA molecules, which contain the four bases that form the genetic code, are copied, base by base, by DNA polymerase enzymes. However, for one of the two DNA strands, a problem exists in that the very end of the strand cannot be copied. Therefore, the chromosomes should be shortened every time a cell divides – but in fact that is not usually the case (Fig 1).

Both these problems were solved when this year's Nobel Laureates discovered how the telomere functions and found the enzyme that copies it.


Telomere DNA protects the chromosomes

In the early phase of her research career, Elizabeth Blackburn mapped DNA sequences. When studying the chromosomes of Tetrahymena, a unicellular ciliate organism, she identified a DNA sequence that was repeated several times at the ends of the chromosomes. The function of this sequence, CCCCAA, was unclear. At the same time, Jack Szostak had made the observation that a linear DNA molecule, a type of minichromosome, is rapidly degraded when introduced into yeast cells.

Blackburn presented her results at a conference in 1980. They caught Jack Szostak's interest and he and Blackburn decided to perform an experiment that would cross the boundaries between very distant species (Fig 2). From the DNA of Tetrahymena, Blackburn isolated the CCCCAA sequence. Szostak coupled it to the minichromosomes and put them back into yeast cells. The results, which were published in 1982, were striking – the telomere DNA sequence protected the minichromosomes from degradation. As telomere DNA from one organism, Tetrahymena, protected chromosomes in an entirely different one, yeast, this demonstrated the existence of a previously unrecognized fundamental mechanism. Later on, it became evident that telomere DNA with its characteristic sequence is present in most plants and animals, from amoeba to man.


An enzyme that builds telomeres

Carol Greider, then a graduate student, and her supervisor Blackburn started to investigate if the formation of telomere DNA could be due to an unknown enzyme. On Christmas Day, 1984, Greider discovered signs of enzymatic activity in a cell extract. Greider and Blackburn named the enzyme telomerase, purified it, and showed that it consists of RNA as well as protein (Fig 3). The RNA component turned out to contain the CCCCAA sequence. It serves as the template when the telomere is built, while the protein component is required for the construction work, i.e. the enzymatic activity. Telomerase extends telomere DNA, providing a platform that enables DNA polymerases to copy the entire length of the chromosome without missing the very end portion.

Blackburn was also the Daily Scan poll favorite. She led the pack with 43 percent of the vote. She was followed by her co-laureate Szostak who garnered 25 percent of the vote.

References:
Szostak JW, Blackburn EH. Cloning yeast telomeres on linear plasmid vectors. Cell 1982; 29:245-255.
Greider CW, Blackburn EH. Identification of a specific telomere terminal transferase activity in Tetrahymena extracts. Cell 1985; 43:405-13.
Greider CW, Blackburn EH. A telomeric sequence in the RNA of Tetrahymena telomerase required for telomere repeat synthesis. Nature 1989; 337:331-7.

Oldest Human ancestor discovered

How old are humans? Until recently I thought Lucy to be our oldest ancestor, that is around 3.9 to 2.9 million years old. A recent finding by a group of scientists reveals our oldest known ancestor "Lucy" is now replaced by "Ardi"(Ardipithecus ramidus) which is older by 1.4 million years than Lucy.This individual, 'Ardi,' was a female who weighed about 50 kilograms and stood about 120 centimetres tall.

In its 2 October 2009 issue, Science presents 11 papers, authored by a diverse international team, describing an early hominid species, Ardipithecus ramidus, and its environment. These 4.4 million year old hominid fossils sit within a critical early part of human evolution, and cast new and sometimes surprising light on the evolution of human limbs and locomotion, the habitats occupied by early hominids, and the nature of our last common ancestor with chimps.

Science is making access to this extraordinary set of materials FREE (non-subscribers require a simple registration). The complete collection, and abridged versions, are available FREE as PDF downloads for AAAS members, or may be purchased as reprints.

The last common ancestor shared by humans and chimpanzees is thought to have lived six or more million years ago. Though Ardipithecus is not itself this last common ancestor, it likely shared many of this ancestor's characteristics. Ardi is closer to humans than chimps. Measuring in at 47 in. (120 cm) tall and 110 lb. (50 kg), Ardi likely walked with a strange gait, lurching side to side, due to lack of an arch in its feet, a feature of later hominids. It had somewhat monkey-like feet, with opposable toes, but its feet were not flexible enough to grab onto vines or tree trunks like many monkeys -- rather they were good enough to provide extra support during quick walks along tree branches -- called palm walking.

Another surprise comes in Ardi's environment. Ardi lived in a lush grassy African woodland, with creatures such as colobus monkeys, baboons, elephants, spiral-horned antelopes, hyenas, shrews, hares, porcupines, bats, peacocks, doves, lovebirds, swifts and owls. Fig trees grew around much of the area, and it is speculated that much of Ardi's diet consisted of these figs.

The surprise about the environment is that it lays to rest the theory that hominids developed upright walking when Africa's woodland-grassland mix changed to grassy savanna. Under this now theory, hominids began standing and walking upright as a way of seeing predators over the tall grasses. The discovery of Ardi -- an earlier upright walker that lived in woodland -- greatly weakens this theory.

Scientists have theorized that Ardi may have formed human-like relationships with pairing between single males and females. Evidence of this is found in the male's teeth, which lack the long canines that gorillas and other non-monogamous apes use to battle for females. Describes Professor Lovejoy, "The male canine tooth is no longer projecting or sharp. It's no longer weaponry."

Restoring your dell laptop to factory setup

I have a dell laptop that had become extremely slow. I was looking at the options of cleaning the resgistry, cleaning up temporary internet files and all other available suggestions on internet. However, this did not help much. My laptop became almost unusable!! My desperate searches in the internet led me to find something like "While booting your system hold your control and f10 key" or "While booting your system hold your control and f11 key" or "While booting your system hold your control and f12 key" to get to setup option. I tried with F10, but it did not work, so I left the rest of the options.

The best way to look for the right combination of keys is to get hold of your DELL manual that comes along with your installation. It is sometimes also called "Owners manual". Once you got hold of it look for the "solving problem" -> "Restoring your operating system" section. Inside this section, you will find specific instructions as to how to get back to your factory default mode. For example mine says the following:

To use PC Restore:
1 Turn on the computer.
During the boot process, a blue bar with www.dell.com appears at the top of the screen.
2 Immediately upon seeing the blue bar, press < Ctrl >< F11 >.
If you do not press < Ctrl >< F11 > in time, let the computer finish starting, and then restart
the computer again.
NOTICE: If you do not want to proceed with PC Restore, click Reboot in the following step.
3 On the next screen that appears, click Restore.
4 On the next screen, click Confirm.
The restore process takes approximately 6–10 minutes to complete.
5 When prompted, click Finish to reboot the computer.
NOTE: Do not manually shut down the computer. Click Finish and let the computer completely
reboot.
6 When prompted, click Yes.
The computer restarts. Because the computer is restored to its original operating state, the
screens that appear, such as the End User License Agreement, are the same ones that
appeared the first time the computer was turned on.
7 Click Next.
The System Restore screen appears and the computer restarts.
8 After the computer restarts, click OK.

And needless to mention this worked like charm!! I got the desirable result. My computer works like new!! Only trouble is, you would have lost all your data and any new installations. But I don't think this is a big price to pay for this great change in performance. Installing softwares as you need and backing up data is something does not take a lot of effort.. So happy restoring your system.

ncftp and wget

As a bioinformatics researcher, it often becomes imperative to download a large amount of data sets from various servers. The most frequent data download site is NCBI and/or EBI. While most of the raw data can be found at NCBI, EBI hosts something much more curated. One nightmare I often face is, updating interproscan database. The data files are something like 9GB and take a lot of time to download, often exceeding the data limit for our server that downloads it. The most irritating of all is when it times out or says "the message list is too long". The best way to handle this trouble could be by using "wget" or "ncftp".

It is very simple to use and is very user friendly. Wget is very reliable and robust. It is specially designed for the home network if you are working from home using an unreliable network. If a download does not complete due to a network problem, Wget will automatically try to continue the download from where it left off, and repeat this until the whole file has been retrieved. It was one of the first clients to make use of the then-new Range HTTP header to support this feature.

If you are using the http protocol with wget then the format will be something like this:
wget --no-check-certificate https://login:passwd@site-address//path
or
wget ftp://ftp.gnu.org/pub/gnu/wget/wget-latest.tar.gz
Or more command info can be found by doing "man wget".

While wget is really cool, ncftp is another ftp protocol, that is sometimes much better than other existing methods, if you must use a ftp protocol for data download. A typical ncftp command could be:

ncftp -u login -p pass ftp://ftp.hostname.edu

Then use get command to get the files of interest.

Finding difference between 2 files

In bioinformatics work, one often encounters something like finding difference between 2 sequence files or finding Union and intersection between 2 datasets. This type of operation often needs comparison of one file with the other.

The most common way to do this job is to loop over first file and then loop over second file finding the match, if found terminate the internal loop reset the file pointer and again move on to the second element in the outer loop. This operation need O(M * N) time space. If size of M and N are huge than the amount of time space requirement is humongous. Using a binary search may reduce the time by O(log2 M * N), but the price will be paid again in sorting the files. If the files are non numeric, then the additional burden of converting strings to numbers will add to the price tag. So, recently, I have found using hash tables for finding an element, instead of looping through a second time is a great substitute for binary search. The files I was trying to search against were each having 2 million records. So, in a serial search, the file would have iterated 2 * 2 miilion times, comparing words 4 million times and resetting file 2 million times. While I was running this using the serial version the program ran for 2 days and was still running. The second approach just few minutes to run. Check this out..

Code for serial search:

#!/usr/bin/perl -w

# This script will find the difference between two list files
# Here find the difference between FH1 - FH2

open FH1, $ARGV[0] or die "Can't open file for reading $!\n";
open FH2, $ARGV[1] or die "Can't open file for reading $!\n";
my $flag = 1;

while(){
$flag = 1;
my $toBeSubtracted = $_;
while(){
if($_ eq $toBeSubtracted){
$flag =0;
last;
}
}
if($flag == 1){
print $toBeSubtracted;
}
seek(FH2,0,0);
}


Code using hash table:
#!/usr/bin/perl -w
# Subtract FH - FH1

open FH, $ARGV[0] or die "Can't open the file to be subtracted$!\n";
open FH1, $ARGV[1] or die "Can't open the file to be subtracted$!\n";


my %hash1;
my %hash2;

while(){
my $tmp = $_;
chomp($tmp);

$hash1{$tmp} = 1;

}
close(FH);

while(){
my $tmp = $_;
chomp($tmp);
$tmp =~ s/\t.*//g;

$hash2{$tmp} = 1;

}
close(FH1);


foreach my $key(keys %hash1){

if(exists($hash2{$key})){
#do nothing
}

else {
print "$key\n";

}

}

Cool unix tips

I work with large scale genome sequences, so I often have to handle huge files. The files many times have some unwanted characters and formats that I try to correct using vim substitute command. Although very powerful, substitution operation on huge files using vim is very time consuming!!

A very powerful option to vim substitution is sed(stream editor) utility. This utility is often under utilized because of lack of proper documentation. Some of very powerful file editing can be done using sed.

Simple Substitution:

sed 's/\/usr\/local\/bin/\/common\/bin/' new # will replace /usr/local/bin by /common/bin

Gulp. Some call this a 'Picket Fence' and it's ugly. It is easier to read if you use an underline instead of a slash as a delimiter:

sed 's_/usr/local/bin_/common/bin_' new

Some people use colons:

sed 's:/usr/local/bin:/common/bin:' new

Others use the "|" character.

sed 's|/usr/local/bin|/common/bin|' new

Replacing a pattern with something else:

In a file if the content is something like: '123 abc' and you want to make it 123 123 abc, then do the following:
sed 's/[0-9]*/& &/' < old > new # Here '&' serves as the pattern

Meaning of \1 and \2 till \9

These simply mean the pattern number. Altogether sed can remember upto 9 patterns.
Suppose say in a file you would like to replace "1234 abcd" by "abcd 1234" then do the following;

sed 's/\([0-9]*\) \([a-z]*\)/\2 \1/'. Here notice the space between the two patterns.

Substituting only in some lime lines then use the following:
sed '101,532 s/A/a/' or sed '101,$ s/A/a/'

Deleting lines beginning with # is easy:

sed '/^#/ d'

This one will remove comments and blank lines:

sed -e 's/#.*//' -e '/^$/ d'

This one will remove all tabs and blanks before end of the line:

sed -e 's/#.*//' -e 's/[ ^I]*$//' -e '/^$/ d'

Before I sign off how to tell if your machine is 64 bit linux or 32 bit?

uname -m
i386 or i686 then it is 32 bit
x86_64 is 64bit

Getting cpu info:

more /proc/cpuinfo

Some more Generic unix shell scripts:

Total number of files in your working area: ls / -R | wc -l &
To see a process you are really interested in seeing: ps ax | grep httpd
To display a tree of processes: pstree
calculator program in unix: bc ( It will wait for your inputs)

Cool Small Applications:
Chessboard:
for (( i = 1; i <= 9; i++ )) ### Outer for loop ### do for (( j = 1 ; j <= 9; j++ )) ### Inner for loop ### do tot=`expr $i + $j` tmp=`expr $tot % 2` if [ $tmp -eq 0 ]; then echo -e -n "\033[47m " else echo -e -n "\033[40m " fi done echo -e -n "\033[40m" #### set back background colour to black echo "" #### print the new line ### done Use Cut and paste command

I find these 2 commands kind of blessing. cut will cut the columns of a flat file for you and paste will merge them together. There is one more command join that joins 2 files if they have a common field...

These utilities are extremely powerful if you are using them in merging 2 or more data files having background corrected/normalized data or something like that. Perl scripts are expansive in terms of file handling.

Using awk:

Did we know that the present day text editor perl burrows most of its syntax from unix shell scripting, particularly from awk.

awk is a powerful pattern matcher having most of its syntax similar to perl or should I say perl has similar syntax as awk.

awk '
BEGIN { actions }
/pattern/ { actions }
/pattern/ { actions }
END { actions }
' files

in the quoted area you can put anything like a condition such as

awk ‘{ if($5 !~ /[0-9]/ || NF != 7) {print “Line Number ” NR “ has error: ” $0}}’ Input file

This command will check if the input file has column 5 non-numeric value and if the input file has 7 fields or not. In case it is not, this is going to print the file having error. In case of no error it just prints nothing. NR is a special character meaning line number. NF stands for number of fields. By default awk takes [tab] as field separator. But in case you have something else as a separator you can begin awk with:


awk '
BEGIN { FS =":"; }
/pattern/ { actions }
/pattern/ { actions }
END { actions }
' files

For example if you are scanning a file for a particular pattern and you want to print that line having the pattern

awk ' BEGIN{FS = ":"; }
/pattern/ {i++; print "Line Number: " NR ":" $0}
END {printf("Number of lines having pattern %d\n",i)}
'

This command will print

Line Number : 1 : suydusydu 88 sllsilsdi
Line Number :5 : suyfdusydu 88 sllsilsdi
Line Number :6 : suycdusydu 88 sllsilsdi
Line Number :7 : suyvdusydu 88 sllsilsdi
Line Number :8 : suygdusydu 88 sllsilsdi
Line Number :9 : suydugsydu 88 sllsilsdi
Line Number :10 : suydusggydu 88 sllsilsdi
Line Number :11 : suydusygdu 88 sllsilsdi
Line Number :12 : suydusgydu 88 sllsilsdi
Line Number :13 : suydusffydu uiuiu sllsilsdi
Line Number :14 : suydusssydu 88 sllsilsdi
Line Number :15 : suydussydu 88 sllsilsdi
Line Number :16 : suydussssydu 88 sllsilsdi
Line Number :17 : suydusssssydu 88 sllsilsdi
Line Number :18 : suydusyssadu 88 sllsilsdi
Line Number :19 : suydusyqqdu 88 sllsilsdi
Line Number :20 : suydusydffu 88 sllsilsdi
Number of lines having pattern 17

DNA Methylation AIDS HIV Latency

Current drug therapies inhibit replication of the human immunodeficiency virus (HIV). In patients undergoing these therapies, the amount of HIV is reduced to an undetectable level and HIV-related disease subsides. However, stopping antiviral drug therapy results in the quick return of HIV and of disease. One reason for this is latently infected cells, in which virus replication is temporarily halted. When drug therapy is stopped, virus from these latently infected cells can resume infection and spread to other cells in the patient, resulting in the return of disease

One of the world's most elusive viruses is an expert at maintaining a low profile, laying dormant in CD4+ cells even during highly active anti-retroviral treatment (HAART). A team of American and Swedish researchers found that the virus might be using DNA methylation as a cloak.

Hypermethylated CpG islands flanking the HIV transcription site attract methyl-CpG binding domain protein 2 (MBD2) -- an endogenous host protein – which in turn recruits histone deacetylaces and other enzymes to shut down transcription.

Using 5-aza-deoxycytidine (Aza-CdR) to strip the DNA methylation at these island allowed researchers to reverse the transcriptional block and reactivate HIV right out of hiding, indicating that Aza-CdR might be a great complement to other antiviral therapies. So there’s hope for flushing out the reservoir, clearing patients of HIV-1, and letting them live a drug free life. Wouldn’t Nancy Reagan be proud?

See all the HAARTening details at PloS Pathogens June 2009.

This is a great article on miRNA

I read this article liked it a lot. So, copy pasting it for future reference:

http://www.nature.com/scitable/topicpage/Small-Non-coding-RNA-and-Gene-Expression-1078

Fungus farmers show way to new drugs

[I came across this interesting story in nature news, thought I will put it in my blog:]

Ant colonies could be key to advances in biofuels and antibiotics.

-Erika Check Hayden

Studies of bacteria on leaf-cutting ants could yield new antibiotics.Studies of bacteria on leaf-cutting ants could yield new antibiotics.M. MOFFETT/FLPA

In a mutually beneficial symbiosis, leaf-cutting ants cultivate fungus gardens, providing both a safe home for the fungi and a food source for the ants. But this 50-million-year-old relationship also includes microbes that new research shows could help speed the quest to develop better antibiotics and biofuels.

Ten years ago, Cameron Currie, a microbial ecologist then at the University of Toronto in Ontario, Canada, discovered that leaf-cutting ants carry colonies of actinomycete bacteria on their bodies (C. R. Currie et al. Nature 398, 701–704; 1999). The bacteria churn out an antibiotic that protects the ants' fungal crops from associated parasitic fungi (such as Escovopsis). On 29 March, Currie, Jon Clardy at the Harvard Medical School in Boston and their colleagues reported that they had isolated and purified one of these antifungals, which they named dentigerumycin, and that it is a chemical that has never been previously reported (D.-C. Oh et al. Nature Chem. Bio. doi: 10.1038/nchembio.159; 2009). The antifungal slowed the growth of a drug-resistant strain of the fungus Candida albicans, which causes yeast infections in people.

“These ants are walking pharmaceutical factories.”

Because distinct ant species cultivate different fungal crops, which in turn fall prey to specialized parasites, researchers hope that they will learn how to make better antibiotics by studying how the bacteria have adapted to fight the parasite in an ancient evolutionary arms race. "These ants are walking pharmaceutical factories," says Currie, now at the University of Wisconsin, Madison.

That's not the end to the possible applications. The ant colonies are also miniature biofuel reactors, Currie reported on 25 March at the Genomics of Energy & Environment meeting at the Joint Genome Institute in Walnut Creek, California. Each year, ants from a single colony harvest up to 400 kilograms of leaves to feed their fungal partners. But no one has worked out how the fungi digest the leaves, because samples of fungus grown in petri dishes can't break down cellulose, a tough molecule found in plant cells. Researchers are keenly interested in better ways to break down cellulose, because it might allow them to make more efficient biofuels than those made from sugary foods, such as maize (corn).

So Currie and his colleagues sequenced small segments of DNA from bacteria and other organisms living in fungus gardens in three Panamanian leaf-cutting ant colonies. They then compared the DNA against databases to identify what species were living in the fungus gardens, and what genes they contained.

This 'metagenomics' approach found that there are many species of bacteria in the fungus gardens that are capable of breaking down cellulose. The team also detected the genetic signatures of fungal enzymes that can break down cellulose, which raises the question of why the fungi can't break down cellulose in the laboratory.

Currie suggests that the newfound bacterial and fungal enzymes might be efficient at digesting cellulose because they have evolved for centuries along with the ant-fungal symbiosis. This could mean that the fungus can only break down cellulose in its natural context, or that the enzymes Currie detected are brought into the colony from outside. "The idea is that the ants' long evolutionary history may help us in our own attempts to break down plant biomass," he says.

Other researchers call Currie's findings interesting, but say they wanted to see a more thorough analysis of the data. "It's interesting that he found these fungal enzymes in the gardens that he didn't expect [based on] what the fungus was capable of doing by itself," says John Taylor, a mycologist at the University of California, Berkeley.

Taylor says that Currie's continued scrutiny of the lives of ants provides insights into the web of interactions necessary for the survival of any single species. "I think the coolest thing about this is that you start with one organism, and then you find more and more organisms involved in the relationship," he says. It may take a village to raise a child; it seems it also takes a village to break down cellulose.

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