Computational Skills Course week 4

1. Computational Skills Courseweek 4 Mike Gilchrist NIMR May-July 2011

2. WEEK FOUR Individual project plans

3. Find the closest transcrip0on start site of a chIP--‐seq peak ? -------+-----------+-----------+----------------+-------+! | chrom | peakStart | peakEnd | macs_peak | score |! +-------+-----------+-----------+----------------+-------+! | chr1 | 6277185 | 6277752 | MACS_peak_2 | 153 |! | chr1 | 12613150 | 12613647 | MACS_peak_6 | 217 |! | chr1 | 12682344 | 12683092 | MACS_peak_7 | 1058 |! | chr1 | 12937069 | 12938092 | MACS_peak_9 | 1136 |! mysql> describe s1_s4_peaks;! +-----------+------------------+------+-----+---------+-------+! | Field | Type | Null | Key | Default | Extra |! +-----------+------------------+------+-----+---------+-------+! | chrom | varchar(255) | YES | | NULL | |! | peakStart | int(10) unsigned | YES | | NULL | |! | peakEnd | int(10) unsigned | YES | | NULL | |! | macs_peak | varchar(255) | YES | | NULL | |! | score | int(10) unsigned | YES | | NULL | |! +-----------+------------------+------+-----+---------+-------+! +--------------+--------------+--------+-----------+-----------+---------+! | name | chrom | strand | txStart | txEnd | name2 |! +--------------+--------------+--------+-----------+-----------+---------+! | NM_028778 | chr1 | + | 134212701 | 134230065 | Nuak2 |! | NM_001195025 | chr1 | + | 134212701 | 134230065 | Nuak2 |! | NM_175370 | chr1 | - | 58714963 | 58752833 | Als2cr12|! | NM_175642 | chr1 | - | 25124320 | 25886552 | Bai3 |! mysql> describe refseq;! +---------+------------------+------+-----+---------+-------+! | Field | Type | Null | Key | Default | Extra |! +---------+------------------+------+-----+---------+-------+! | name | varchar(255) | YES | | NULL | |! | chrom | varchar(255) | YES | | NULL | |! | strand | char(1) | YES | | NULL | |! | txStart | int(10) unsigned | YES | | NULL | |! | txEnd | int(10) unsigned | YES | | NULL | |! | name2 | varchar(255) | YES | | NULL | |! +---------+------------------+------+-----+---------+-------+! For each row (each peaks) in s1_s4_peaks I want the closest name (name2) in refseq Laurent

4. Veronique Aims of the project : - Comparing the transcriptional profiles of forelimbs and hindlimbs over an embryonic time-course: - Identify additional candidates “limb-type modifiers” - Compare the transcriptional profile dynamics between FL and HL of common forelimb/hindlimb GRNs to establish a limb-type signature RNA seq -Reads from solexa Alignment on the transcriptome Normalization Analysis of the transcriptional dynamic of chosen gene regulatory networks

5. Guilherme Our lab generated an hypomorphic Lhx6 allele which expresses reduced levels of mRNA. This allele specifically affects differentiation of a subset of cortical interneurons. This results in the development of seizures. These unique mutants allow the study of mechanisms of specification of cortical interneuron subtypes and the generation of seizures. After P60: Spontaneous Seizures From about E11.5: reduced Lhx6 levels P15: mRNA-Seq experiment mRNA extracted from cortex +/- : controls (4) -/-: nulls (3) LacZ/-: hypomorphs (3) Questions: 1) Molecular processes affected 2) Molecular markers for cell types affected From E13.5: reduced Sst levels P40 - P60: Physiological defects at inhibitory dendritic synapses

6. George How do binding sites for the T-box transcription factor Brachyury change over time during frog embryogenesis?

7. James Data • mRNA-seq in chick neural cells + and - Shh • database of chick transcription factors (TFs) • ChIP-seq analysis identifying binding sites of several TFs in mouse neural cells responding to Shh Analysis • map mRNA-seq data to chick genome • measure gene expression levels and identify differentially expressed genes • identify subset of regulated genes that are TFs • identify mouse orthologs of differentially expressed TFs (and genes) • identify clusters of TFs binding near regulated genes • ask whether there is (i) any enrichment for clusters of binding sites near regulated genes; (ii) any correlation between combination of Tfs bound and type of regulation; (iii) predictive value in the ChIP-seq data for the regulation of gene expression.

8. Siggi The ‘PROJECT’ - Siggi Sato (Parasitology) Gene expression in P. falciparum Nuclear genome Identify genetic elements determining the limit of the intron Protein/RNA factors for splicing and controlling organelles Organellar genomes (Plastid, Mitochondrion) Identify genetic elements for replication and transcription New anti-P. falciparum Finding new substances and identifying their targets Alaremycin (patent filed) MRC-T “small molecules” ( Others? )

9. Ashleigh The molecular regulation of IL-10 and IL-12 in innate cells: Investigating the differential production of IL-10 and IL-12 in commonly used inbred mouse strains • C57BL/6 and BALB/c macrophages produce reciprocal levels of IL-10 and IL-12 when stimulated with bacterial products. This could influence their responses to infection. Other inbred mouse strains also differentially regulate IL-10 and IL-12. • WHY? • To compliment wet lab experiments, we would like to use re-sequencing data generated by ourselves and by the Sanger Institute Mouse Genomes Project to hone in on genetic differences in 5 different mouse strains that could contribute to these phenotypes (starting with candidate loci based on our in vitro studies). http://www.ensembl.org/Mus_musculus/Location/ • Key initial questions include: • Are there differences (SNPs/deletions) in the IL-10/IL-12/type • I IFN loci? • Are these differences in regulatory elements (TF binding • sites/3’UTR) or protein coding regions?

10. Alex Are there any genes in the Xenopus tropicalis genome that do not have a corresponding EST in the Xenopus laevisdatabase?& Vice versa

11. Jose DB IMGT RF http://imgt.cines.fr/cgi-bin/IMGTlect.jv?query=5+AB019437 AC AB019437 SP Human GL IGHV GN V7-81 NA caggtgcagctggtgcagtctggccatgaggtgaagcagcctggggcctcagtgaaggtc NA tcctgcaaggcttctggttacagtttcaccacctatggtatgaattgggtgccacaggcc NA cctggacaagggcttgagtggatgggatggttcaacacctacactgggaacccaacatat NA gcccagggcttcacaggacggtttgtcttctccatggacacctctgccagcacagcatac NA ctgcagatcagcagcctaaaggctgaggacatggccatgtattactgtgcgagata AA QVQLVQSGHEVKQPGASVKVSCKASGYSFTTYGMNWVPQAPGQGLEWMGWFNTYTGNPTY AA AQGFTGRFVFSMDTSASTAYLQISSLKAEDMAMYYCAR // Flat file database of mouse and human sequences from databases IMGT, ABG, NCBI and VBASE2 in EMBL format. Load (how?) into MySQL (database design: tables and primary key?) Remove redundancy in sequences but retain pointers to other fields. Flexible query and output different sets of sequences for e.g. blast search.

12. IL-10 Associated Histone Modification Pattern of T helper Subsets Leona IL-2 FACS purify CD4+CD44loCD25-Foxp3GFP-10BiT- T cells from SPN aIL-4, aIL-6, aIL-12, aIFN-g, aTGFb ChIP-Seq: Histone Modification IL-4 IL-10 IL-4, aIFN-g, aIL-12 FACS purify CD4+10BiT+ vs CD4+10BiT- T cells IL-12, aIL-4 IFN-g IL-10 TGFb, IL-2 10BiT (IL-10 reporter) Foxp3GFP TCR7 Rag1-/- Culture with: - HEL peptide - DCs - Skewing cytokines/ blocking antibodies TGFb, IL-6,aIFN-g, aIL-4 TGFb IL-10 List of histone marks of all genes in the different subsets IL-17 IL-10 IL-10? Histone Modification Pattern Maps 10BiT+ 10BiT- Tbet Foxp3 Rorgt Gata3 Identify differences in histone patterns between IL-10 secreting vs non-secreting T helper cells Compare histone patterns in the different T helper cell subsets; gaining insight into “housekeeping” vs activation vs lineage defining genes Assign histone patterns to genes in the different T helper cell subsets

13. Mustafa • Define All Exons in X. tropicalis • How? • Combine current gene models, transcriptome assemblies, available and soon to be available RNA-seq • Why? • Genome sequence/annotation - imperfect • Exon Capture Sequencing – mutant gene identification • Gap Capture – gene model improvements • Analysis of RNA-seq data • When? • Tomorrow would be good Khokha Lab – Computational Goal

14. Mary

15. Madhu One exercise I am trying now is to predict the potential PfSUB2 (a subtilisin-like protease with relative sequence-specificity at the cleavage site) in Plasmodium falciparum protein database. I downloaded predicted  protein sequences for one chromosome (Chr 13) and  by using grep detected 38 sequences. After editing output with sed to make it look like a fasta file (line numbers as identifiers for each sequence), queried against the chr13 protein database (blastp for max_target_seqs 1) to obtain accession numbers.I am yet to write/try any script.What I would like to do: I have a Pfmsp7 knock out parasite line which shows invasion phenotype. Once the technical hurdles are passed (like getting rid off rRNA sequences with polyA in it), we want to RNAseq analyse in order to identify genes that may have been affected.