TWF2 (NM 007284) Human Tagged ORF Clone Product Data

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TWF2 (NM 007284) Human Tagged ORF Clone Product Data

OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RC201791L2 TWF2 (NM_007284) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: TWF2 (NM_007284) Human Tagged ORF Clone Tag: mGFP Symbol: TWF2 Synonyms: A6r; A6RP; MSTP011; PTK9L Vector: pLenti-C-mGFP (PS100071) E. coli Selection: Chloramphenicol (34 ug/mL) Cell Selection: None ORF Nucleotide The ORF insert of this clone is exactly the same as(RC201791). Sequence: Restriction Sites: SgfI-MluI Cloning Scheme: ACCN: NM_007284 ORF Size: 1047 bp This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 2 TWF2 (NM_007284) Human Tagged ORF Clone – RC201791L2 OTI Disclaimer: The molecular sequence of this clone aligns with the gene accession number as a point of reference only. However, individual transcript sequences of the same gene can differ through naturally occurring variations (e.g. polymorphisms), each with its own valid existence. This clone is substantially in agreement with the reference, but a complete review of all prevailing variants is recommended prior to use. More info OTI Annotation: This clone was engineered to express the complete ORF with an expression tag. Expression varies depending on the nature of the gene. RefSeq: NM_007284.3 RefSeq Size: 1659 bp RefSeq ORF: 1050 bp Locus ID: 11344 UniProt ID: Q6IBS0 Domains: ADF Protein Families: Druggable Genome MW: 39.5 kDa Gene Summary: The protein encoded by this gene was identified by its interaction with the catalytic domain of protein kinase C-zeta. The encoded protein contains an actin-binding site and an ATP-binding site. It is most closely related to twinfilin (PTK9), a conserved actin monomer-binding protein. [provided by RefSeq, Jul 2008] This product is to be used for laboratory only. Not for diagnostic or therapeutic use. ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 2 / 2.

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PARSANA-DISSERTATION-2020.Pdf
DECIPHERING TRANSCRIPTIONAL PATTERNS OF GENE REGULATION: A COMPUTATIONAL APPROACH by Princy Parsana A dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland July, 2020 © 2020 Princy Parsana All rights reserved Abstract With rapid advancements in sequencing technology, we now have the ability to sequence the entire human genome, and to quantify expression of tens of thousands of genes from hundreds of individuals. This provides an extraordinary opportunity to learn phenotype relevant genomic patterns that can improve our understanding of molecular and cellular processes underlying a trait. The high dimensional nature of genomic data presents a range of computational and statistical challenges. This dissertation presents a compilation of projects that were driven by the motivation to efficiently capture gene regulatory patterns in the human transcriptome, while addressing statistical and computational challenges that accompany this data. We attempt to address two major difficulties in this domain: a) artifacts and noise in transcriptomic data, andb) limited statistical power. First, we present our work on investigating the effect of artifactual variation in gene expression data and its impact on trans-eQTL discovery. Here we performed an in-depth analysis of diverse pre-recorded covariates and latent confounders to understand their contribution to heterogeneity in gene expression measurements. Next, we discovered 673 trans-eQTLs across 16 human tissues using v6 data from the Genotype Tissue Expression (GTEx) project. Finally, we characterized two trait-associated trans-eQTLs; one in Skeletal Muscle and another in Thyroid. Second, we present a principal component based residualization method to correct gene expression measurements prior to reconstruction of co-expression networks.
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