1. Lab7 Twinscan, HMMER, PFAM

2. TWINSCAN

3. TwinScan TwinScan finds genes in a "target" genomic sequence by simultaneously maximizing the probability of the gene structure in the target and the evolutionary conservation derived from "informant" genomic sequences. The target sequence (i.e. the sequence to be annotated) should generally be of draft or finished quality. The informant can range from a single sequence to a whole genome in any condition from raw shotgun reads to finished assembly. References – http://mblab.wustl.edu/media/publications/Hu-Brent-2003-Proof.pdf – http://mrw.interscience.wiley.com/emrw/9780471250951/cp/cpbi/article/bi0 408/current/abstract – http://bioinformatics.oxfordjournals.org/cgi/content/abstract/17/suppl_1/S14 0

4. Requirements TwinScan/ Nscan 4.0 executable DNA sequence in FASTA format Twinscan parameter file (/parameters/twinscan_parameters) – Each filename contains the name of the target organism: eg maize_twinscan.zhmm. Conservation sequence (see examples/example.conseq) – A symbolic representation of the the best alignments between the target and informant sequences. – To create this conservation sequence, WU-BLAST (http://blast.wustl.edu) is used. – For NCBI BLAST, the input parameters need to be changed. (see parameters/examples/example_blast_parameters.txt) – xdformat (WU-BLAST package) formats the informant sequences to create a blast database. – After running BLAST, the output must be formatted with conseq, which is included in this package. – Example (using WU-BLAST) xdformat -n informant.fa Blast M=1 N=-1 Q=5 R=1 B=10000 V=100 -cpus=1 -warnings -lcfilter filter=seg filter=dust topcomboN=1 informant.fa target.fa > blast.out conseq target.fa blast.out > conseq.fa – Note: The runTwinscan2 script will run these steps without user intervention (see below). EST sequence (optional) – EST sequence is a symbolic representation of evidence from ESTs that align to the target sequence. – The estseq script included in the distribution creates EST sequence when given a DNA sequence and a (set of) BLAT reports of the the ESTs aligned to the target. – For downloading BLAT, go to http://genome.ucsc.edu/FAQ/FAQblat.html#blat3

5. Web service and Installation Web service – http://mblab.wustl.edu/nscan/submit/ http://mblab.wustl.edu/nscan/submit/ Local installation – http://mblab.wustl.edu/software/twinscan/ Installation step $ tar xvzf iscan-4.1.2.tar.gz $ cd N-SCAN $ make linux $./test-executable

6. How to run Usage twinscan -c= [- e=estseq_file] > Example:./bin/iscan./parameters/twinscan_parameters/human_twinscan.zhmm./examples/example.fa.masked -c=./examples/example.conseq > mySequence.gtf

7. Running Twinscan using the runTwinscan2 script In summary, there are 5 steps required to run Twinscan: – Step 1: Mask target sequence with RepeatMasker – Step 2: Create informant BLAST database – Step 3: Run BLAST – Step 4: Create conservation sequence – (Step 4b: Create EST sequence) – Step 5: Run Twinscan These five steps are all contained in the example script runTwinscan2 (see./bin) The default BLAST parameters used by runTwinscan2 are those for C.elegans (see parameters/blast_params/Celegans.blast.param). – This can and should be changed for any other species with the -B option to the runTwinscan2 script. The file example.output in the /examples directory contains the output from runTwincan2 using the BLAST parameters found in the script.

8. Local environment setting for runTwinscan2 Example –../bin/runTwinscan2 -r../parameters/twinscan_parameters/human_twinscan.zhmm -d output -B../parameters/blast_params/Hsapiens.blast.param example.fa.masked informant.fa After running you can find output files in the newly created /output directory. Several programs must be installed on your system to run runTwinscan. You may need to change runTwinscan to point it to these programs. – my $REPEATMASKER = "RepeatMasker"; # Format for local environment – my $BLASTN = "blastn"; # Format for local environment – my $BLAT = "blat"; # Format for local environment – my $XDFORMAT = "xdformat"; # Format for local environment – my $PRESSDB = "pressdb"; # Format for local environment

9. HMMER

10. Sean Eddy’s Lab http://selab.janelia.org/software.html

11. 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/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

12. 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.

13. 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;

14. 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.

15. 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.

16. 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.

17. PFAM

18. 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.ADDA – 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)

19. 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 – http://darwin.informatics.indiana.edu/col/courses/I529- 12/Lab/Lab7/Data/PFAM_data/ – Copy to your working director 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”;