Spectral Jaccard Similarity: A New Approach to Estimating Pairwise Sequence Alignments.

Pairwise sequence alignment is often a computational bottleneck in genomic analysis pipelines, particularly in the context of third-generation sequencing technologies. To speed up this process, the pairwise -mer Jaccard similarity is sometimes used as a proxy for alignment size in order to filter pairs of reads, and min-hashes are employed to efficiently estimate these similarities. However, when the -mer distribution of a dataset is significantly non-uniform (e.

Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq.

Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. We observe that because clustering "forces" separation, reusing the same dataset generates artificially low p values and hence false discoveries.

Random access in large-scale DNA data storage.

Synthetic DNA is durable and can encode digital data with high density, making it an attractive medium for data storage. However, recovering stored data on a large-scale currently requires all the DNA in a pool to be sequenced, even if only a subset of the information needs to be extracted. Here, we encode and store 35 distinct files (over 200 MB of data), in more than 13 million DNA oligonucleotides, and show that we can recover each file individually and with no errors, using a random access approach.

HINGE: long-read assembly achieves optimal repeat resolution.

Long-read sequencing technologies have the potential to produce gold-standard de novo genome assemblies, but fully exploiting error-prone reads to resolve repeats remains a challenge. Aggressive approaches to repeat resolution often produce misassemblies, and conservative approaches lead to unnecessary fragmentation. We present HINGE, an assembler that seeks to achieve optimal repeat resolution by distinguishing repeats that can be resolved given the data from those that cannot.

Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts.

Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling, which limits their scope and generality. We propose a novel method that compares and clusters cells based on their transcript-compatibility read counts rather than on the transcript or gene quantifications used in standard analysis pipelines. In the reanalysis of two landmark yet disparate single-cell RNA-seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.