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Biology subjects

Kaymaz, Y.

Publications and source records attributed to Kaymaz, Y..

4 recordsLinked to original sources

PeakATail: precision-first poly(A)-site calling and calibrated alternative polyadenylation analysis in single-cell RNA-seq

Most human genes can end their messenger RNAs at more than one position, and the choice shifts between cell types. Single-cell sequencing reads that choice in individual cells, but two questions are seldom asked: are the reported sites real, and does the test for a cell-type difference hold its error rate? Here we introduce our tool PeakATail, which calls polyadenylation sites from reads still carrying a piece of the tail, ranks them by how many distinct molecules carry that evidence, discards sites where adenosine-rich genomic sequence mimics a tail, and pairs them with a differential test whose error rate is measured, not assumed. The evidence is scarce, so the tool is built to be right rather than complete. Across four libraries from two species, 71 to 83 percent of its calls fall within 100 bases of a curated atlas, over thirty times the shuffled-control rate; long reads from different donors support 76.5 percent of the human calls. The cost is sensitivity. Only one of six test configurations held its false-positive rate under shuffled labels; with it, 15,942 cell-type switches replicated across twelve lung cancer patients and none in ten shuffled-label runs. One pre-registered target was missed and is reported.

genomics↗

HieRFIT: Hierarchical Random Forest for Information Transfer

The emergence of single-cell RNA sequencing (scRNA-seq) has led to an explosion in novel methods to study biological variation among individual cells, and to classify cells into functional and biologically meaningful categories. Here, we present a new cell type projection tool, HieRFIT (Hierarchical Random Forest for Information Transfer), based on hierarchical random forests. HieRFIT uses a priori information about cell type relationships to improve classification accuracy, taking as input a hierarchical tree structure representing the class relationships, along with the reference data. We use an ensemble approach combining multiple random forest models, organized in a hierarchical decision tree structure. We show that our hierarchical classification approach improves accuracy and reduces incorrect predictions especially for inter-dataset tasks which reflect real life applications. We use a scoring scheme that adjusts probability distributions for candidate class labels and resolves uncertainties while avoiding the assignment of cells to incorrect types by labeling cells at internal nodes of the hierarchy when necessary. Using HieRFIT, we re-analyzed publicly available scRNA-seq datasets showing its effectiveness in cell type cross-projections with inter/intra-species examples. HieRFIT is implemented as an R package and it is available at (https://github.com/yasinkaymaz/HieRFIT/releases/tag/v1.0.0)

bioinformatics↗

Evaluating single-cell cluster stability using the Jaccard similarity index

MotivationOne major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor, and the resolution parameters, among others. ResultsHere, we present a set of tools to evaluate cluster stability by subsampling, which can guide parameter choice and aid in biological interpretation. The R package scclusteval and the accompanying Snakemake workflow implement all steps of the pipeline: subsampling the cells, repeating the clustering with Seurat, and estimation of cluster stability using the Jaccard similarity index. The Snakemake workflow takes advantage of high-performance computing clusters and dispatches jobs in parallel to available CPUs to speed up the analysis. The scclusteval package provides functions to facilitate the analysis of the output, including a series of rich visualizations. AvailabilityR package scclusteval: https://github.com/crazyhottommy/scclusteval Snakemake workflow: https://github.com/crazyhottommy/pyflow_seuratv3_parameter Contacttsackton@g.harvard.edu, tangming2005@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Epstein Barr virus genomes reveal population structure and type 1 association with endemic Burkitt lymphoma

Endemic Burkitt lymphoma (eBL), the most prevalent pediatric cancer in sub-Saharan Africa, is associated with malaria and Epstein Barr virus (EBV). In order to better understand the role of EBV in eBL, we improved viral DNA enrichment methods and generated a total of 98 new EBV genomes from both eBL cases (N=58) and healthy controls (N=40) residing in the same geographic region in Kenya. Comparing cases and controls, we found that EBV type 1 was significantly associated with eBL with 74.5% of patients (41/55) versus 47.5% of healthy children (19/40) carrying type 1 (OR=3.24, 95% CI=1.36 - 7.71, P=0.007). Controlling for EBV type, we also performed a genome-wide association study identifying 6 nonsynonymous variants in the genes EBNA1, EBNA2, BcLF1, and BARF1 that were enriched in eBL patients. Additionally, we observed that viruses isolated from plasma of eBL patients were identical to their tumor counterpart consistent with circulating viral DNA originating from the tumor. We also detected three intertypic recombinants carrying type 1 EBNA2 and type 2 EBNA3 regions as well as one novel genome with a 20 kb deletion resulting in the loss of multiple lytic and virion genes. Comparing EBV types, genes show differential variation rates as type 1 appears to be more divergent. Besides, type 2 demonstrates novel substructures. Overall, our findings address the complexities of EBV population structure and provide new insight into viral variation, which has the potential to influence eBL oncogenesis.\n\nKey PointsO_LIEBV type 1 is more prevalent in eBL patients compared to the geographically matched healthy control group.\nC_LIO_LIGenome-wide association analysis between cases and controls identifies 6 eBL-associated nonsynonymous variants in EBNA1, EBNA2, BcLF1, and BARF1 genes.\nC_LIO_LIAnalysis of population structure reveals that EBV type 2 exists as two genomic sub groups.\nC_LI

genomics↗