Scientific Area: Genetic Variation Analysis

Presenting author: Chrysanthi Ainali, Life Technologies, Germany

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Ion Reporter™ Software is a hosted suite of informatics tools that streamlines and simplifies analysis, annotation, and archiving of semiconductor sequencing data. Designed specifically for researchers performing routine sequencing assays by automating the variant analysis bioinformatics pipeline, Ion Reporter™ Software detects variants and connects them with public annotations, allowing faster biological assessment. The workflow steps mirror the typical role-based sample processing workflow used in production labs. The software provides functionality for germline and low frequency variant calling in single samples, tumor and matched normal paired samples to identify somatic variants based on integrated evidence from matched tumor and normal samples, as well trio analysis. Different analysis modules used in the various workflows include alignment, mapping statistics, on and off target statistics, SNP and INDEL calling. The Tumor/Normal algorithm calls variants in the tumor sample, analyzing reads from the normal sample to remove variants with elevated background at the tumor variant position. The Tumor/Normal module was run on an 80:20 mixture of two HapMap samples (simulating low frequency somatic variants), and on a variety of real cancer samples. Ion Reporter Software supports family analysis with an algorithm for inference of inherited disease, which was used to analyze a HapMap trio on both the Ion AmpliSeq™ Inherited Disease Panel and with Ion TargetSeq Exome Kit™ sequencing runs of the full trio on the Ion PI™ chip. Ion Reporter™ Software provides an optimal solution for analysis of semiconductor sequencing data, as well as integration with third party software for case-control or pathway analyses.

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Scientific Area: Computational aspects

Presenting author: Verena Schuetz, Biomax Informatics AG, Germany

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The BioXM™ platform of Biomax is a highly flexible, configurable and integrative data and knowledge management system. Its generalizing approach enables semantic modeling of domain-specific knowledge and allows for combining user-relevant results with publicly available knowledge from external databases. Due to easy and fast configuration customer-tailored applications range from pharmaceutical and clinical research, biobanking, next-generation sequencing, sequence analysis, pathway analysis, literature mining, systems biology to comparative genomics.

Together with DSM we have extended the BioXM platform to the field of synthetic biology, which aims at exploiting engineering principles of abstraction and standardization to design novel complex biological systems. BioXM now provides an infrastructure for sequence-centered applications easily accessible through a user-friendly, configurable Wiki platform. It serves as a repository for standard biological parts such as promoters, ORFs or terminators that can be visually assembled into constructs following a set of design specification rules to ensure correctness. To account for context dependency as an inherent characteristic of biological systems, a key advantage is that constructs can be characterized by wet lab information enabling collaborative feedback that gives valuable knowledge for future construct design.

Together with the connection to external databases such as Pubmed and Uniprot and integration of public or proprietary sequence analysis tools like EMBOSS, codon optimization algorithms, BLAST, ClustalW as well as the possibility to query the system for relevant biological and functional information, this novel extension of the BioXM platform will streamline strain optimization and demonstrates its unique capability to meet the needs of the life sciences industry.

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Scientific Area: Bioinformatics of Disease and Treatment

Presenting author: Sivakumar Gowrisankar, Novartis Institutes for BioMedical Research, United States

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High-throughput genetic profiling of tumor tissues especially those that are formalin fixed and paraffin embedded (FFPE) are highly limited by sensitivity and specificity of assays. This has been due to a wide array of issues such as low DNA starting material, DNA degradation, tumor heterogeneity to name a few. Several methods have been proposed to profile mutations within tumor samples such as the targeted hybrid capture and PCR-based amplicons enrichment. Hybridization based approaches have the caveat of requiring higher input starting material and complicated workflows. Most PCR-based approaches have been known to suffer from high false positives due to the inability to remove PCR-duplicates. On the other hand whole-genome and exome sequencing are still prohibitively expensive to employ on large-scale studies to characterize tumor samples.

We here present a tumor profiling approach based on PCR-based amplification of selected genes followed by next-generation sequencing. We first randomly barcode PCR-products by adaptor ligation, followed by PCR-amplification and subsequent sequencing. This approach has the distinct advantages of requiring lower DNA starting material, simple workflow, and ability to distinguish PCR-duplicates. In addition the uniquely barcoded reads can be used to reduce false positives. The high correlation of read distribution between tumor-normal or tumor-resistant tumor samples yield itself to reliable copy number variant (CNV) detection. In this poster we provide the results on sensitivity, specificity and CNV detection on 24 paired and pooled control samples to demonstrate the utility of this approach.

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