Lab7

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Lab7

• QRNA, HMMER, PFAM

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Sean Eddys Lab

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

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HMMER
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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

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HMMER executables

1. 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.

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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

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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.

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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.

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Hmmsearch - search a sequence database with a
profile HMM

• hmmsearch options hmmfile seqfile
• hmmsearch test.hmm query.faa gt 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.

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PFAM
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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)

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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/PFA
  M_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 gt test.faa.pfam
• The results is logged into an output file
  test.faa.pfam

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QRNA
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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.

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Local Installation

• The latest version of QRNA (qrna-2.0.3c.tar.gz )
  can be download from ftp//selab.janelia.org/pub/s
  oftware/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/

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• Data location
• /home/kwchoi/public_html/I529-09-lab/Lab7/Data/QRN
  A_data/
• blastn2qrnadepth.pl
• /home/kwchoi/Installed/qrna-2.0.3c/src/scripts/bla
  stn2qrnadepth.pl -g human HG_13_RNAs_gene.fa.MGSCv
  3.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/l
  ib
• /home/kwchoi/Installed/qrna-2.0.3c/src/eqrna -a
  5s_rRNA.q gt 5s_rRNA.q.eqrna

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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 gt 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 gt
  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