L'Écho : grand quotidien d'information du Centre Ouest

09 décembre 19401940/12/09 (A69)-1940/12/10.Appartient à l’ensemble documentaire : PoitouCh

Additional file 7: Figure S7. of Complete haplotype phasing of the MHC and KIR loci with targeted HaploSeq

Targeted HaploSeq generates high quality phasing of heterozygous genes. Over 92 % of exonic het. variants are phased at an accuracy of 99 %. (TIFF 8219 kb

Additional file 3: Figure S3. of Complete haplotype phasing of the MHC and KIR loci with targeted HaploSeq

Targeted HaploSeq data has large pool of long insert fragments. a) Insert-size distribution of targeted Haploseq (green) and b) HaploSeq (purple) in GM12878 LCLs. Both these datasets have similar amount of long-insert fragments which is critical for long range haplotyping. (TIFF 8219 kb

Additional file 6: Figure S6. of Complete haplotype phasing of the MHC and KIR loci with targeted HaploSeq

Targeted HaploSeq generates a single (complete) haplotype structure across MHC/KIR locus. The performance metric of the Targeted HaploSeq protocol, measured by completeness (span of the haplotype bloc), resolution (fraction of het. alleles resolved), and accuracy. While each of these metrics were defined after performing read-based as well as population based haplotyping, seed resolution is estimated only based on read-based haplotyping. The overall resolution is defined as the weighted average among all alleles accross the MHC and KIR loci together. We observe over 50 % decrease in error rate from 2.3 to 1.06 % after correcting for potential incorrect local haplotypes from parent-trio data. (TIFF 8219 kb

The structure of the HMM with <i>n</i> transcription factors (TFs).

<p>It is composed of <i>n</i> TF blocks and two background states. Between TF blocks and a background block is a branch. Each PSSM block is labeled with an alphabet. Background states are labeled with ‘x’. To model a forward and reverse PSSM, a PSSM block has 2<i>s</i>+2 states inside, where <i>s</i> is the length of a PSSM.</p

ROC curves for the cis-module predication.

<p>The prediction performance of COMET <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005501#pone.0005501-Frith2" target="_blank">[10]</a>, Cluster-Buster <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005501#pone.0005501-Frith3" target="_blank">[11]</a>, Stubb <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005501#pone.0005501-Sinha1" target="_blank">[15]</a> and the proposed HMM approach are compared.</p

Assigned value on each label based on the ChIP-chip ratio.

<p>Assigned value on each label based on the ChIP-chip ratio.</p

Binding motifs for the four TFs used in establishing the CRM.

<p>The sequence logos were generated using WebLogo <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005501#pone.0005501-Crooks1" target="_blank">[30]</a>.</p

Dataset of 4 TFs.

<p>Dataset of 4 TFs.</p

A Scalable Epitope Tagging Approach for High Throughput ChIP-Seq Analysis

Eukaryotic transcriptional factors (TFs) typically recognize short genomic sequences alone or together with other proteins to modulate gene expression. Mapping of TF-DNA interactions in the genome is crucial for understanding the gene regulatory programs in cells. While chromatin immunoprecipitation followed by sequencing (ChIP-Seq) is commonly used for this purpose, its application is severely limited by the availability of suitable antibodies for TFs. To overcome this limitation, we developed an efficient and scalable strategy named cmChIP-Seq that combines the clustered regularly interspaced short palindromic repeats (CRISPR) technology with microhomology mediated end joining (MMEJ) to genetically engineer a TF with an epitope tag. We demonstrated the utility of this tool by applying it to four TFs in a human colorectal cancer cell line. The highly scalable procedure makes this strategy ideal for ChIP-Seq analysis of TFs in diverse species and cell types