Biology subjects

Costa, H. A.

Publications and source records attributed to Costa, H. A..

4 recordsLinked to original sources

bioRxiv2020

NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.

bioinformatics↗

bioRxiv2020

STAMPede: A Precision Oncology Catalogue of Solid Tumors

Molecular profiling of tumor specimens is a key contributor in the application of precision medicine toward patient care in oncology. The presence of genetic mutational data may provide insight into the etiology of the cancer and inform about the available therapeutic options for patients diagnosed with various cancer types. However, the ability to centrally organize, structure, and visualize this genetic data can be hindered by the lack of necessary computational infrastructure. Here we present a somatic tumor data visualization portal titled STAMPede that utilizes tumor sequencing data from an in-house solid tumor oncology sequencing panel at Stanford Health Care. STAMPede is intended to provide Stanford healthcare providers and clinical researchers an easy to navigate web-based portal to query and display gene-, variant-, and cancer-level summary statistics.

genomics↗

bioRxiv2019

Development and clinical validation of a targeted RNAseq panel (Fusion-STAMP) for diagnostic and predictive gene fusion detection in solid tumors

RNA sequencing is emerging as a powerful technique to detect a diverse array of fusions in human neoplasia, but few clinically validated assays have been described to date. We designed and validated a hybrid-capture RNAseq assay for FFPE tissue (Fusion-STAMP). It fully targets the transcript isoforms of 43 genes selected for their known impact as actionable targets of existing and emerging anti-cancer therapies (especially in lung adenocarcinomas), prognostic features, and/or utility as diagnostic cancer biomarkers (especially in sarcomas). 57 fusion results across 34 samples were evaluated. Fusion-STAMP demonstrated high overall accuracy with 98% sensitivity and 94% specificity for fusion detection. There was high intra- and inter-run reproducibility. Detection was sensitive to approximately 10% tumor, though this is expected to be impacted by fusion transcript expression levels, hybrid capture efficiency, and RNA quality. Challenges of clinically validating RNA sequencing for fusion detection include a low average RNA quality in FFPE specimens, and variable RNA total content and expression profile per cell. These challenges contribute to highly variable on-target rates, total read pairs, and total mapped read pairs. False positive results may be caused by intergenic splicing, barcode hopping / index hopping, or misalignment. Despite this, Fusion-STAMP demonstrates high overall performance metrics for qualitative fusion detection and is expected to provide clinical utility in identifying actionable fusions.

genomics↗

bioRxiv2018

Structural variation detection by proximity ligation from FFPE tumor tissue

The clinical management and therapy of many solid tumor malignancies is dependent on detection of medically actionable or diagnostically relevant genetic variation. However, a principal challenge for genetic assays from tumors is the fragmented and chemically damaged state of DNA in formalin-fixed paraffin-embedded (FFPE) samples. From highly fragmented DNA and RNA there is no current technology for generating long-range DNA sequence data as is required to detect genomic structural variation or long-range genotype phasing. We have developed a high-throughput chromosome conformation capture approach for FFPE samples that we call \"Fix-C\", which is similar in concept to Hi-C. Fix-C enables structural variation detection from fresh and archival FFPE samples. We applied this method to 15 clinical adenocarcinoma and sarcoma specimens spanning a broad range of tumor purities. In this panel, Fix-C analysis achieves a 90% concordance rate with FISH assays - the current clinical gold standard. Additionally, we are able to identify novel structural variation undetected by other methods and recover long-range chromatin configuration information from these FFPE samples harboring highly degraded DNA. This powerful approach will enable detailed resolution of global genome rearrangement events during cancer progression from FFPE material, and inform the development of targeted molecular diagnostic assays for patient care.

genomics↗