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Find video protocols related to scientific articles indexed in Pubmed.
Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia.
Blood
PUBLISHED: 06-02-2014
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Genome sequencing has revealed a large number of shared and personal somatic mutations across human cancers. In principle, any genetic alteration affecting a protein-coding region has the potential to generate mutated peptides that are presented by surface HLA class I proteins that might be recognized by cytotoxic T cells. To test this possibility, we implemented a streamlined approach for the prediction and validation of such neoantigens derived from individual tumors and presented by patient-specific HLA alleles. We applied our computational pipeline to 91 chronic lymphocytic leukemias (CLLs) that underwent whole-exome sequencing (WES). We predicted ?22 mutated HLA-binding peptides per leukemia (derived from ?16 missense mutations) and experimentally confirmed HLA binding for ?55% of such peptides. Two CLL patients that achieved long-term remission following allogeneic hematopoietic stem cell transplantation were monitored for CD8(+) T-cell responses against predicted or confirmed HLA-binding peptides. Long-lived cytotoxic T-cell responses were detected against peptides generated from personal tumor mutations in ALMS1, C6ORF89, and FNDC3B presented on tumor cells. Finally, we applied our computational pipeline to WES data (N = 2488 samples) across 13 different cancer types and estimated dozens to thousands of predicted neoantigens per individual tumor, suggesting that neoantigens are frequent in most tumors.
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SOX2 and p63 colocalize at genetic loci in squamous cell carcinomas.
J. Clin. Invest.
PUBLISHED: 03-03-2014
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The transcription factor SOX2 is an essential regulator of pluripotent stem cells and promotes development and maintenance of squamous epithelia. We previously reported that SOX2 is an oncogene and subject to highly recurrent genomic amplification in squamous cell carcinomas (SCCs). Here, we have further characterized the function of SOX2 in SCC. Using ChIP-seq analysis, we compared SOX2-regulated gene profiles in multiple SCC cell lines to ES cell profiles and determined that SOX2 binds to distinct genomic loci in SCCs. In SCCs, SOX2 preferentially interacts with the transcription factor p63, as opposed to the transcription factor OCT4, which is the preferred SOX2 binding partner in ES cells. SOX2 and p63 exhibited overlapping genomic occupancy at a large number of loci in SCCs; however, coordinate binding of SOX2 and p63 was absent in ES cells. We further demonstrated that SOX2 and p63 jointly regulate gene expression, including the oncogene ETV4, which was essential for SOX2-amplified SCC cell survival. Together, these findings demonstrate that the action of SOX2 in SCC differs substantially from its role in pluripotency. The identification of the SCC-associated interaction between SOX2 and p63 will enable deeper characterization the downstream targets of this interaction in SCC and normal squamous epithelial physiology.
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A pan-cancer analysis of transcriptome changes associated with somatic mutations in U2AF1 reveals commonly altered splicing events.
PLoS ONE
PUBLISHED: 01-01-2014
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Although recurrent somatic mutations in the splicing factor U2AF1 (also known as U2AF35) have been identified in multiple cancer types, the effects of these mutations on the cancer transcriptome have yet to be fully elucidated. Here, we identified splicing alterations associated with U2AF1 mutations across distinct cancers using DNA and RNA sequencing data from The Cancer Genome Atlas (TCGA). Using RNA-Seq data from 182 lung adenocarcinomas and 167 acute myeloid leukemias (AML), in which U2AF1 is somatically mutated in 3-4% of cases, we identified 131 and 369 splicing alterations, respectively, that were significantly associated with U2AF1 mutation. Of these, 30 splicing alterations were statistically significant in both lung adenocarcinoma and AML, including three genes in the Cancer Gene Census, CTNNB1, CHCHD7, and PICALM. Cell line experiments expressing U2AF1 S34F in HeLa cells and in 293T cells provide further support that these altered splicing events are caused by U2AF1 mutation. Consistent with the function of U2AF1 in 3' splice site recognition, we found that S34F/Y mutations cause preferences for CAG over UAG 3' splice site sequences. This report demonstrates consistent effects of U2AF1 mutation on splicing in distinct cancer cell types.
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Comparative analysis of RNA sequencing methods for degraded or low-input samples.
Nat. Methods
PUBLISHED: 02-18-2013
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RNA-seq is an effective method for studying the transcriptome, but it can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations or cadavers. Recent studies have proposed several methods for RNA-seq of low-quality and/or low-quantity samples, but the relative merits of these methods have not been systematically analyzed. Here we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and compared them against two control libraries. We found that the RNase H method performed best for chemically fragmented, low-quality RNA, and we confirmed this through analysis of actual degraded samples. RNase H can even effectively replace oligo(dT)-based methods for standard RNA-seq. SMART and NuGEN had distinct strengths for measuring low-quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development.
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PB1-F2 Finder: scanning influenza sequences for PB1-F2 encoding RNA segments.
BMC Bioinformatics
PUBLISHED: 11-30-2011
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PB1-F2 is a major virulence factor of influenza A. This protein is a product of an alternative reading frame in the PB1-encoding RNA segment 2. Its presence of is dictated by the presence or absence of premature stop codons. This virulence factor is present in every influenza pandemic and major epidemic of the 20th century. Absence of PB1-F2 is associated with mild disease, such as the 2009 H1N1 ("swine flu").
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Database resources for proteomics-based analysis of cancer.
Methods Mol. Biol.
PUBLISHED: 03-04-2011
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Biological/bioinformatics databases are essential for medical and biological studies. They integrate and organize biologically related information in a structured format and provide researchers with easy access to a variety of relevant data. This review presents an overview of publicly available databases relevant to proteomics studies in cancer research. They include gene/protein expression databases, gene mutation and single nucleotide polymorphisms databases, tumor antigen databases, protein-protein interaction, and biological pathway databases. Automated information retrieval from these databases enables efficient large-scale proteomics data analysis.
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Data processing and analysis for protein microarrays.
Methods Mol. Biol.
PUBLISHED: 03-04-2011
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Protein microarrays are a high-throughput technology capable of generating large quantities of proteomics data. They can be used for general research or for clinical diagnostics. Bioinformatics and statistical analysis techniques are required for interpretation and reaching biologically relevant conclusions from raw data. We describe essential algorithms for processing protein microarray data, including spot-finding on slide images, Z score, and significance analysis of microarrays (SAM) calculations, as well as the concentration dependent analysis (CDA). We also describe available tools for protein microarray analysis, and provide a template for a step-by-step approach to performing an analysis centered on the CDA method. We conclude with a discussion of fundamental and practical issues and considerations.
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MULTIPRED2: a computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles.
J. Immunol. Methods
PUBLISHED: 09-03-2010
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MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes. NetMHCpan and NetMHCIIpan are used as prediction engines. The 13 HLA Class I supertypes are A1, A2, A3, A24, B7, B8, B27, B44, B58, B62, C1, and C4. The 13 HLA Class II DR supertypes are DR1, DR3, DR4, DR6, DR7, DR8, DR9, DR11, DR12, DR13, DR14, DR15, and DR16. In total, MULTIPRED2 enables prediction of peptide binding to 1077 variants representing 26 HLA supertypes. MULTIPRED2 has visualization modules for mapping promiscuous T-cell epitopes as well as those regions of high target concentration - referred to as T-cell epitope hotspots. Novel graphic representations are employed to display the predicted binding peptides and immunological hotspots in an intuitive manner and also to provide a global view of results as heat maps. Another function of MULTIPRED2, which has direct relevance to vaccine design, is the calculation of population coverage. Currently it calculates population coverage in five major groups in North America. MULTIPRED2 is an important tool to complement wet-lab experimental methods for identification of T-cell epitopes. It is available at http://cvc.dfci.harvard.edu/multipred2/.
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Novel sequence feature variant type analysis of the HLA genetic association in systemic sclerosis.
Hum. Mol. Genet.
PUBLISHED: 11-18-2009
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We describe a novel approach to genetic association analyses with proteins sub-divided into biologically relevant smaller sequence features (SFs), and their variant types (VTs). SFVT analyses are particularly informative for study of highly polymorphic proteins such as the human leukocyte antigen (HLA), given the nature of its genetic variation: the high level of polymorphism, the pattern of amino acid variability, and that most HLA variation occurs at functionally important sites, as well as its known role in organ transplant rejection, autoimmune disease development and response to infection. Further, combinations of variable amino acid sites shared by several HLA alleles (shared epitopes) are most likely better descriptors of the actual causative genetic variants. In a cohort of systemic sclerosis patients/controls, SFVT analysis shows that a combination of SFs implicating specific amino acid residues in peptide binding pockets 4 and 7 of HLA-DRB1 explains much of the molecular determinant of risk.
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High-throughput minor histocompatibility antigen prediction.
Bioinformatics
PUBLISHED: 07-01-2009
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Minor histocompatibility antigens (mHags) are a diverse collection of MHC-bound peptides that have immunological implications in the context of allogeneic transplantation because of their differential presence in donor and host, and thus play a critical role in the induction of the detrimental graft-versus-host disease (GvHD) or in the development of the beneficial graft-versus-leukemia (GvL) effect. Therefore, the search for mHags has implications not only for preventing GvHD, but also for therapeutic applications involving leukemia-specific T cells. We have created a web-based system, named PeptideCheck, which aims to augment the experimental discovery of mHags using bioinformatic means. Analyzing peptide elution data to search for mHags and predicting mHags from polymorphism and protein databases are the core features.
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MaHCO: an ontology of the major histocompatibility complex for immunoinformatic applications and text mining.
Bioinformatics
PUBLISHED: 05-07-2009
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The high level of polymorphism associated with the major histocompatibility complex (MHC) poses a challenge to organizing associated bioinformatic data, particularly in the area of hematopoietic stem cell transplantation. Thus, this area of research has great potential to profit from the ongoing development of biomedical ontologies, which offer structure and definition to MHC-data related communication and portability issues.
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Mutated BCR-ABL generates immunogenic T-cell epitopes in CML patients.
Clin. Cancer Res.
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Characterization of an approach to identify leukemia neoantigens arising in the context of drug resistance.
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RNA-SeQC: RNA-seq metrics for quality control and process optimization.
Bioinformatics
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RNA-seq, the application of next-generation sequencing to RNA, provides transcriptome-wide characterization of cellular activity. Assessment of sequencing performance and library quality is critical to the interpretation of RNA-seq data, yet few tools exist to address this issue. We introduce RNA-SeQC, a program which provides key measures of data quality. These metrics include yield, alignment and duplication rates; GC bias, rRNA content, regions of alignment (exon, intron and intragenic), continuity of coverage, 3/5 bias and count of detectable transcripts, among others. The software provides multi-sample evaluation of library construction protocols, input materials and other experimental parameters. The modularity of the software enables pipeline integration and the routine monitoring of key measures of data quality such as the number of alignable reads, duplication rates and rRNA contamination. RNA-SeQC allows investigators to make informed decisions about sample inclusion in downstream analysis. In summary, RNA-SeQC provides quality control measures critical to experiment design, process optimization and downstream computational analysis. Availability and implementation: See www.genepattern.org to run online, or www.broadinstitute.org/rna-seqc/ for a command line tool.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.