Chris Sander

Bio: Chris Sander is an academic researcher from Harvard University. The author has contributed to research in topics: Large Hadron Collider & Protein structure. The author has an hindex of 178, co-authored 713 publications receiving 233287 citations. Previous affiliations of Chris Sander include Purdue University & University of Leeds.

Abstract 5140: Individual patient cancer profiles in the cBio Cancer Genomic Portal.

Abstract: The most prominent or most interesting genomic alteration events from an individual tumor sample can now be browsed and analyzed in the cBio Cancer Genomics Portal. With the rapid accumulation of detailed and comprehensive genomic maps of thousands of tumors in The Cancer Genome Atlas and other projects it has now become feasible to nominate the functionally most significant events affecting a tumor from an individual patient. The cBio Cancer Genomics Portal (http://cbioportal.org) allows interactive exploration of multidimensional cancer genomics data sets, currently for more than 6,000 tumor samples from 20 cancer genomics studies, including all TCGA projects. This information gateway significantly lowers the barrier between complex genomic data and their efficient use by cancer researchers for the development of biologic insights and clinical applications. In addition to gene-by-gene alteration maps across many samples and across diverse tumor types, one can now view genomic alterations in individual tumor samples. As there are potentially hundreds or thousands of genomic alterations in any single tumor sample, it is crucially important to select, for inspection and analysis, alteration events most likely to contribute to oncogenesis or affect the response to therapy. In the cBio portal patient view, this selection is done making use of recurrence statistics, background functional knowledge and predicted functional impact, under the control of the cancer researcher. All relevant data about a tumor are displayed on a single page, including clinical characteristics, summaries of the extent of mutations and copy-number alterations, as well as details about mutated, amplified, and deleted genes. Genomic alterations are filtered by the following criteria: recurrence of mutations or copy-number alterations across the tumor cohort (MutSig and GISTIC), mutation occurrence in COSMIC, and by cancer gene annotation (via the Sanger Cancer Gene Census and other sources). The patient view also provides information about drugs that target the altered genes and lists relevant clinical trials. The patient view is fully interactive and enables quick and easy assessment of all relevant genomic events in individual tumor samples. All data can be viewed in the context of the other tumors in the cohort, which facilitates classification of tumor samples by genomic criteria and can supplement standard pathology. Once characterization of genomic alterations in tumor samples becomes standard practice in patient care, this tool could be used to assess prognosis and guide treatment decisions, ideally the personalized choice of targeted therapies. Citation Format: Jianjiong Gao, Selcuk Onur Sumer, Gideon Dresdner, Bulent Arman Aksoy, Chris Sander, Nikolaus Schultz. Individual patient cancer profiles in the cBio Cancer Genomic Portal. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5140. doi:10.1158/1538-7445.AM2013-5140

Abstract 1284: How can you interpret gene lists from -omics experiments

Abstract: Understanding the mechanisms responsible for a cellular behaviour often begins with observations of genes and gene products. Depending on the type of experiment, the number of resulting genes can be small, but increasingly, researchers are faced with many thousands of measurements, as in the case of transcriptomic or protein-DNA binding observations. Here, we describe ways to pair experimental results consisting of one or more genes with analysis tools with the overall aim being to make results more biologically interpretable. In certain cases, experimental approaches such as screens for essential genes can generate one or a few ‘genes of interest9 and there is a desire to understand their relationship to one another as well as discover links to additional, interesting genes. To this end, ‘GeneMANIA9 is a web tool that accepts gene names and returns a network visualization of related genes based on similarity in expression, localization, protein domains and those involved in physical interactions. Likewise, ‘PCViz9 is a web tool that displays a network of interactions drawn from Pathway Commons, a web resource for pathway and interaction knowledge. In cases where experiments generate a lengthy list of genes, for instance, transcriptomic measurements, there is a desire to understand their relevance to a phenotype of interest. Pathway enrichment analysis methods aim to summarize gene lists as pathways, which have a closer link to cell function. An online ‘Guide9 by Pathway Commons includes workflows that illustrate how to chain together software tools to identify pathways from the corresponding gene-level data then organize and summarize the pathway-level results in an interactive visualization known as an Enrichment Map. For those wishing to drill-down to individual pathways, Pathway Commons offers a set of web apps, including ‘Search9 that enables users to query by keyword and visualize ranked search results. Ongoing development of web apps aims to enhance the accessibility to pathways and integrate support for analysis and visualization of experimental data. The full complement of data, tools and resources offered by Pathway Commons in support of pathway analysis are described. Citation Format: Jeffrey V. Wong, Augustin Luna, Emek Demir, Igor Rodchenkov, Ozgun Babur, Chris Sander, Gary D. Bader. How can you interpret gene lists from -omics experiments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1284.

Jaws. A film of enzyme dynamics

Abstract: Offprint requests to C Sander dominate atomic displacements and that surprisingly few of these suffice to describe experimentally determined fluctuations of atomic position We have now calculated the normal mode dynamics of the enzymes lysozyme and ribonuclease [2] A film of their lowest frequency normal modes shows opening and closing of the active site cleft This jawlike behaviour is due to the collective motion of entire protein domains and is likely to facilitate catalysis We are led to the suggestion that optimization of enzyme function in natural evolution or genetic engineering must affect not only the nature and positioning of active site residues but also the nature and positioning of key residues modulating domain dynamics

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

Abstract: The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice

Abstract: The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools.

Abstract: CLUSTAL X is a new windows interface for the widely-used progressive multiple sequence alignment program CLUSTAL W. The new system is easy to use, providing an integrated system for performing multiple sequence and profile alignments and analysing the results. CLUSTAL X displays the sequence alignment in a window on the screen. A versatile sequence colouring scheme allows the user to highlight conserved features in the alignment. Pull-down menus provide all the options required for traditional multiple sequence and profile alignment. New features include: the ability to cut-and-paste sequences to change the order of the alignment, selection of a subset of the sequences to be realigned, and selection of a sub-range of the alignment to be realigned and inserted back into the original alignment. Alignment quality analysis can be performed and low-scoring segments or exceptional residues can be highlighted. Quality analysis and realignment of selected residue ranges provide the user with a powerful tool to improve and refine difficult alignments and to trap errors in input sequences. CLUSTAL X has been compiled on SUN Solaris, IRIX5.3 on Silicon Graphics, Digital UNIX on DECstations, Microsoft Windows (32 bit) for PCs, Linux ELF for x86 PCs, and Macintosh PowerMac.

MUSCLE: multiple sequence alignment with high accuracy and high throughput

Abstract: We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the logexpectation score, and refinement using treedependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

Gene Ontology: tool for the unification of biology

Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.