Lab7

QRNA, HMMER, PFAM

Sean Eddy’s Lab

• http://selab.janelia.org/software.html

HMMER

Introduction

• HMMER2 is an implementation in UNIX (Linux, MacOS) platform of profile hidden Markov model, whose source code, executables, and user guide can be downloaded from http://hmmer.janelia.org/

• The experiment of HMMER is to look for known domains in a query sequence by searching a single sequence again a library of HMMs.

• One such library is PFAM, and you can also create your own library using HMMER

HMMER executables1. hmmalign ‐ Align sequences to an existing model.

2. hmmbuild Build a model from a multiple sequence alignment.‐

3. hmmcalibrate Takes an HMM and empirically determines parameters that are used to make ‐searches more sensitive, by calculating more accurate expectation value scores (E values).‐

4. hmmconvert Convert a model file into different formats, including a compact HMMER 2 binary ‐format, and “best effort” emulation of GCG profiles.

5. hmmemit Emit sequences probabilistically from a profile HMM.‐

6. hmmfetch Get a single model from an HMM database.‐

7. hmmindex Index an HMM database‐ .

8. hmmpfam Search an HMM database for matches to a query ‐ sequence.

9. hmmsearch Search a sequence database for matches to an HMM.‐

Installation• Simple installation

– Download the current version of HMMER “hmmer 2.3.2.bin.intel linux.tar.gz” ‐ ‐ from http://hmmer.janelia.org/#download;

– Unpack the software by typing “tar –xvf hmmer 2.3.2.bin.intel linux.tar.gz” in ‐ ‐ the command line. You will see a new directory “hmmer 2.3.2.bin.intel linux”.‐ ‐

– Enter the directory of hmmer 2.3.2.bin.intel linux. You will see NINE executables ready in the subdirectory ‐ ‐“/binaries”, and also nine files in the subdirectory “/tutorial”;

• Installation from source code– Download the current HMMER source code version “hmmer 2.3.2.tar.gz” from ‐

http://hmmer.janelia.org/#download;– Create a new directory in your Linux account and upload or move the software package to the directory;– Unpack the software by typing “tar –xvf hmmer 2.3.2.tar.gz” in the command ‐ line;– Type “cd hmmer 2.3.2” to enter the software directory;‐– Type “./configure” to configure for your system and build the programs;– Type “make” to generate the executables;– Type “make check” to run the automated test suite; (This is optional but recommended, and all these tests

should pass);– Please note that by default programs are in “/usr/local/bin/” and man pages are in “/usr/local/man/man1”;– Type “make install” to install all executables;

Hmmbuild : build a profile HMM from an aignment

• hmmbuild [options] hmmfile alignfile • hmmbuild test.hmm test.aln• hmmbuild -h

• hmmbuild reads a multiple sequence alignment file alignfile , builds a new profile HMM, and saves the HMM in hmmfile.

• alignfile may be in ClustalW, GCG MSF, or SELEX alignment format.

• By default, the model is configured to find one or more non-overlapping alignments to the complete model.

• To configure the model for a single global alignment, use the -g option;

• To configure the model for multiple local alignments, use the -f option;

• To configure the model for a single local alignment (standard Smith/Waterman), use the -s option.

Hmmcalibrate: calibrate HMM search statistics

• hmmcalibrate [options] hmmfile• hmmcalibrate test.hmm• Hmmcalibrate -h

• hmmcalibrate reads an HMM file from hmmfile, scores a large number of synthesized random sequences with it, fits an extreme value distribution (EVD) to the histogram of those scores, and re-saves hmmfile now including the EVD parameters.

• This step is optional, but it will increase the sensitivity of your database search

• hmmcalibrate may take several minutes (or longer) to run. While it is running, a temporary file called hmmfile.xxx is generated in your working directory.

• If you abort hmmcalibrate prematurely (ctrl-C, for instance), your original hmmfile will be untouched, and you should delete the hmmfile.xxx temporary file.

Hmmsearch: - search a sequence database with a profile HMM

• hmmsearch [options] hmmfile seqfile • hmmsearch test.hmm query.faa > query.faa.domain• hmmsearch -h

• hmmsearch reads an HMM from hmmfile and searches seqfile for significantly similar sequence matches.

• hmmsearch may take minutes or even hours to run, depending on the size of the sequence database. It is a good idea to redirect the output to a file.

• The output consists of four sections: • a ranked list of the best scoring sequences, • a ranked list of the best scoring domains, • alignments for all the best scoring domains, and • a histogram of the scores.

• A sequence score may be higher than a domain score for the same sequence if there is more than one domain in the sequence; the sequence score takes into account all the domains. All sequences scoring above the -E and -T cutoffs are shown in the first list, then every domain found in this list is shown in the second list of domain hits. If desired, E-value and bit score thresholds may also be applied to the domain list using the -domE and -domT options.

PFAM

Pfam 23.0 (July 2008, 10340 families)• The Pfam database is a large collection of protein families, each represented by multiple sequence

alignments and hidden Markov models (HMMs).

• Proteins are generally composed of one or more functional regions, commonly termed domains. Different combinations of domains give rise to the diverse range of proteins found in nature.

• The identification of domains that occur within proteins can therefore provide insights into their function.

• There are two components to Pfam: Pfam-A and Pfam-B. – Pfam-A entries are high quality, manually curated families. – Although these Pfam-A entries cover a large proportion of the sequences in the underlying sequence

database, in order to give a more comprehensive coverage of known proteins we also generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. – Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A

entries are found.

• Pfam also generates higher-level groupings of related families, known as clans. A clan is a collection of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM. (see Pfam-C)

Sequence analysis with HMM• ftp://ftp.sanger.ac.uk/pub/databases/Pfam/releases/Pfam23.0/ to

download files “Pfam_fs.gz” and “Pfam_ls.gz”– Pfam_ls - All global (ls mode) Pfam-A HMMs in an HMM library searchable

with the hmmpfam program. – Pfam_fs - All local (fs mode) Pfam-A HMMs in an HMM library searchable with

the hmmpfam program.

• Data location– /home/kwchoi/public_html/I529-09-lab/Lab7/Data/PFAM_data/

– Copy to your working directory or make symbolic link

• To search for domains in “test.faa” in the global sequence database, type – hmmpfam Pfam_fs test.faa > test.faa.pfam”

• The results is logged into an output file “test.faa.pfam”;

QRNA

QRNA• QRNA is a prototype structural noncoding RNA genefinder tools for

detecting novel structural RNA genes.

• It uses three probabilistic "pair-grammars": • a pair stochastic context free grammar modeling alignments

constrained by structural RNA evolution, • a pair hidden Markov model modeling alignments constrained by

coding sequence evolution, and • a pair hidden Markov model modeling a null hypothesis of position-

independent evolution.

• Given an input pairwise sequence alignment (e.g. from a BLASTN comparison of two related genomes), it classify the alignment into the coding, RNA, or null class according to the posterior probability of each class.

Local Installation• The latest version of QRNA (qrna-2.0.3c.tar.gz ) can be download from

ftp://selab.janelia.org/pub/software/qrna/

• Configure QRNA and install• tar -xvf qrna-2.0.3c.tar• cd qrna-2.0.3c• cd squid• make• cd ../squid02• make• cd ../src• make

• QRNA is installed in • /home/kwchoi/Installed/qrna-2.0.3c/

• Data location:– /home/kwchoi/public_html/I529-09-lab/Lab7/Data/

QRNA_data/

• blastn2qrnadepth.pl– /home/kwchoi/Installed/qrna-2.0.3c/src/scripts/

blastn2qrnadepth.pl -g human HG_13_RNAs_gene.fa.MGSCv3.fragchrom.blast• HG_13_RNAs_gene.fa.MGSCv3.fragchrom.blast.E0.01.D1.q• HG_13_RNAs_gene.fa.MGSCv3.fragchrom.blast.E0.01.D1.q.gff• HG_13_RNAs_gene.fa.MGSCv3.fragchrom.blast.E0.01.D1.q.rep

• Simple test– Set the running enveriment and run a simple example:

• export QRNADB=/home/kwchoi/Installed/qrna-2.0.3c/lib• /home/kwchoi/Installed/qrna-2.0.3c/src/eqrna -a 5s_rRNA.q >

5s_rRNA.q.eqrna

QRNA demo

• Option -C shuffles the columns of the pairwise alignment while maintaining the gap and conserved structure of the original alignment. Compare the two results of using -C and without using -C:– /home/kwchoi/Installed/qrna-2.0.3c/src/eqrna -a -C 5s_rRNA.q >

5s_rRNA.q.con_shuffle.eqrna

• Example using the scanning version with a window. Consider file “Scerevisiae orf v other yeasts.q” which contains an alignment of a S. cerevisiae ORF The alignment has 514 nucleotides, and we would like to score it with eqrna using a window of 150 nucleotides, and moving the window 50 nucleotides each time:– /home/kwchoi/Installed/qrna-2.0.3c/src/eqrna -w 150 -x 50

Scerevisiae_orf_v_other_yeasts.q > Scerevisiae_orf_v_other_yeasts.q.w150.x50.eqrna

• example start with a blastn output: – /home/kwchoi/Installed/qrna-2.0.3c/src/eqrn -w 50 -x 50

HG_13_RNAs_gene.fa.MGSCv3.fragchrom.blast.E0.01.D1.q HG_13_RNAs_gene.fa.MGSCv3.fragchrom.blast.E0.01.D1.q.W150.X50.eqrna

• The results files are shown as following:– 5s_rRNA.q.eqrna– 5s_rRNA.q.con_shuffle.eqrna– Scerevisiae_orf_v_other_yeasts.q.w150.x50.eqrna