Biology subjects

Kubit, M.

Publications and source records attributed to Kubit, M..

4 recordsLinked to original sources

bioRxiv2021

Characterization of the consensus mucosal microbiome of colorectal cancer

Recent evidence suggests that dysbiosis, an imbalance of microbiota, is associated with increased risk of colorectal cancer. Diverse microbial organisms are physically associated with the cells found in tumor biopsies. Characterizing this mucosa-associated microbiome through genome sequencing has advantages compared to culture-based profiling. However, there are notable challenges in accurately characterizing the features of tumor microbiomes with methods like transcriptome sequencing. Most sequence reads originate from the host. Moreover, there is a high likelihood of bacterial contaminants being introduced. Another major challenge is the microbiome diversity among different studies. Colorectal tumors demonstrate a significant extent of microbiome variation among individuals from different geographic and ethnic origins. To address these challenges, we identified a consensus microbiome for colorectal cancer through analyzing 924 tumors from eight independent RNA-Seq data sets. A standardized meta-transcriptomic analysis pipeline was established and applied to the complete CRC cohort. Common contaminants were filtered out. Our study involved taxonomic investigation of non-human sequences, linked microbial signatures to phenotypes and the association of microbiome with tumor microenvironment components. Microbiome profiles across different CRC cohorts were compared, and recurrently altered microbial shifts specific to CRC were determined. We identified cancer-specific set of 114 microbial species associated with tumors that were found among all investigated studies. Validating our approach, we found that Fusobacterium nucleatum was one of the most enriched bacterial species in CRC. Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria were among the four most abundant phyla for CRC microbiome. Signficant associations between the consensus species and specific immune cell types were noted. Our results are available as a web data resource for other researchers to explore (https://crc-microbiome.stanford.edu).

genomics↗

bioRxiv2019

Overcoming high nanopore basecaller error rates for DNA storage via basecaller-decoder integration and convolutional codes

As magnetization and semiconductor based storage technologies approach their limits, bio-molecules, such as DNA, have been identified as promising media for future storage systems, due to their high storage density (petabytes/gram) and long-term durability (thousands of years). Furthermore, nanopore DNA sequencing enables high-throughput sequencing using devices as small as a USB thumb drive and thus is ideally suited for DNA storage applications. Due to the high insertion/deletion error rates associated with basecalled nanopore reads, current approaches rely heavily on consensus among multiple reads and thus incur very high reading costs. We propose a novel approach which overcomes the high error rates in basecalled sequences by integrating a Viterbi error correction decoder with the basecaller, enabling the decoder to exploit the soft information available in the deep learning based basecaller pipeline. Using convolutional codes for error correction, we experimentally observed 3x lower reading costs than the state-of-the-art techniques at comparable writing costs. The code, data and Supplementary Material is available at https://github.com/shubhamchandak94/nanopore_dna_storage.

bioengineering↗

bioRxiv2019

Whole genome analysis identifies the association of TP53 genomic deletions with lower survival in Stage III colorectal cancer

DNA copy number aberrations (CNA) were frequently observed in colorectal cancers (CRC). There is an urgent call for CNA-based biomarkers in clinics, in particular for Stage III CRC, if combined with imaging or pathologic evidence, promise more precise care at the timing. We conducted this Stage III specific biomarker discovery with a cohort of 134 CRCs, and with a newly developed high-efficiency CNA profiling protocol. Specifically, we developed the profiling protocol for tumor-normal matched tissue samples based on low-coverage clinical whole-genome sequencing (WGS). We demonstrated the protocols accuracy and robustness by a systematic benchmark with microarray, high-coverage whole-exome and -genome approaches, where the low-coverage WGS-derived CNA segments were highly accordant (PCC>0.95) with those derived from microarray, and they were substantially less variable if compared to exome-derived segments. A lasso-based model and multivariate cox regression analysis identified a chromosome 17p loss, containing the TP53 tumor suppressor gene, that was significantly associated with reduced survival (P=0.0139, HR=1.688, 95% CI = [1.112-2.562]), which was validated by an independent cohort of 187 Stage III CRCs. In summary, the new low-coverage WGS protocol has high sensitivity, high resolution and low cost and the identified 17p-loss is an effective poor prognosis marker for Stage III patients.

genomics↗

bioRxiv2019

Improved read/write cost tradeoff in DNA-based data storage using LDPC codes

With the amount of data being stored increasing rapidly, there is significant interest in exploring alternative storage technologies. In this context, DNA-based storage systems can offer significantly higher storage densities (petabytes/gram) and durability (thousands of years) than current technologies. Specifically, DNA has been found to be stable over extended periods of time which has been demonstrated in the analysis of organisms long since extinct. Recent advances in DNA sequencing and synthesis pipelines have made DNA-based storage a promising candidate for the storage technology of the future.\n\nRecently, there have been multiple efforts in this direction, focusing on aspects such as error correction for synthesis/sequencing errors and erasure correction for handling missing sequences. The typical approach is to use separate codes for handling errors and erasures, but there is limited understanding of the efficiency of this framework. Furthermore, the existing techniques use short block-length codes and heavily rely on read consensus, both of which are known to be suboptimal in coding theory.\n\nIn this work, we study the tradeoff between the writing and reading costs involved in DNA-based storage and propose a practical scheme to achieve an improved tradeoff between these quantities. Our scheme breaks with the traditional separation framework and instead uses a single large block-length LDPC code for both erasure and error correction. We also introduce novel techniques to handle insertion and deletion errors introduced by the synthesis process. For a range of writing costs, the proposed scheme achieves 30-40% lower reading costs than state-of-the-art techniques on experimental data obtained using array synthesis and Illumina sequencing.\n\nThe code, data, and Supplementary Material is available at https://github.com/shubhamchandak94/LDPC_DNA_storage.

bioengineering↗