BioBloom tools: fast, accurate and memory-efficient host species sequence screening using bloom filters

Chu, Justin; Sadeghi, Sara; Raymond, Anthony; Jackman, Shaun D.; Nip, Ka Ming; Mar, Richard; Mohamadi, Hamid; Butterfield, Yaron S.N.; Robertson, A. Gordon; Birol, İnanç

doi:10.1093/bioinformatics/btu558

Cited by 107 publications

(91 citation statements)

References 9 publications

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“…In the first stage we classify read sequences using a pipeline based on BBT (Release 1.2.10), a fast Bloom filter-based method (Chu et al, 2014). For 48-bp PE RNAseq data, we processed data for 408 tumor samples and 19 tissue normal samples; for 76-bp PE WES data we processed 412 tumor samples and 429 blood or tissue normals; and for 51-bp PE WGS data (high and low pass) we processed 136 tumor samples and 145 blood or tissue normal samples.…”

Section: Star Methodsmentioning

confidence: 99%

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Robertson¹,

Kim²,

Al‐Ahmadie³

et al. 2017

Cell

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Summary We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival ‘neuronal’ subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, lncRNA, and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma-in-situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

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Section: Star Methodsmentioning

confidence: 99%

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Robertson¹,

Kim²,

Al‐Ahmadie³

et al. 2017

Cell

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1,807

1,624

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“…The microbial detection pipeline is based on BioBloomTools (BBT, v1.2.4.b1), which is a Bloom filter-based method for rapidly classifying RNA-seq or DNA-seq read sequences (Chu et al, 2013). We generated 43 filters from ‘complete’ NCBI genome reference sequences of bacteria, viruses, fungi and protozoa, using 25-bp k-mers and a false positive rate of 0.02.…”

Section: Methodsmentioning

confidence: 99%

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

Robertson¹,

Shih²,

Yau³

et al. 2017

Cancer Cell

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SUMMARY Comprehensive multiplatform analysis of 80 uveal melanomas (UM) identifies four molecularly distinct, clinically relevant subtypes: two associated with poor-prognosis monosomy 3 (M3) and two with better-prognosis disomy 3 (D3). We show that BAP1 loss follows M3 occurrence and correlates with a global DNA methylation state that is distinct from D3-UM. Poor-prognosis M3-UM divide into subsets with divergent genomic aberrations, transcriptional features, and clinical outcomes. We report change-of-function SRSF2 mutations. Within D3-UM, EIF1AX- and SRSF2/SF3B1 -mutant tumors have distinct somatic copy number alterations and DNA methylation profiles, providing insight into the biology of these low-versus intermediate-risk clinical mutation subtypes.

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“…We also looked for the presence of viral pathogens in PTC using two independent methods to assess the RNA-seq data: PathSeq (Kostic et al, 2011) and BioBloom Tools (Chu et al, 2014). We identified two tumors with hepatitis B virus (HBV) and one tumor with human papillomavirus 45 (HPV45) at relative frequencies exceeding 0.1 viral reads per million human reads (RPM) for PathSeq and 0.2 RPM for BBT (see Supplement and Tables S4G-I), indicating that viral pathogens are unlikely significant contributors to PTC pathogenesis.…”

Section: Resultsmentioning

confidence: 99%

Integrated Genomic Characterization of Papillary Thyroid Carcinoma

Agrawal¹,

Akbani²,

Aksoy³

et al. 2014

Cell

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Summary Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic alterations (relative to other carcinomas) and extended the set of known PTC driver alterations to include EIF1AX, PPM1D and CHEK2 and diverse gene fusions. These discoveries reduced the fraction of PTC cases with unknown oncogenic driver from 25% to 3.5%. Combined analyses of genomic variants, gene expression, and methylation demonstrated that different driver groups lead to different pathologies with distinct signaling and differentiation characteristics. Similarly, we identified distinct molecular subgroups of BRAF-mutant tumors and multidimensional analyses highlighted a potential involvement of oncomiRs in less-differentiated subgroups. Our results propose a reclassification of thyroid cancers into molecular subtypes that better reflect their underlying signaling and differentiation properties, which has the potential to improve their pathological classification and better inform the management of the disease.

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BioBloom tools: fast, accurate and memory-efficient host species sequence screening using bloom filters

Cited by 107 publications

References 9 publications

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

Integrated Genomic Characterization of Papillary Thyroid Carcinoma

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