Single-cell RNA sequencing (scRNA-seq) is widely used for analyzing gene expression in multi-cellular systems and provides unprecedented access to cellular heterogeneity. scRNA-seq experiments aim to identify and quantify all cell types present in a sample. Measured single-cell transcriptomes are grouped by similarity and the resulting clusters are mapped to cell types based on cluster-specific gene expression patterns. While the process of generating clusters has become largely automated, annotation remains a laborious ad-hoc effort that requires expert biological knowledge. Here, we introduce CellMeSH - a new automated approach to identifying cell types for clusters based on prior literature. CellMeSH combines a database of gene-cell type associations with a probabilistic method for database querying. The database is constructed by automatically linking gene and cell type information from millions of publications using existing indexed literature resources. Compared to manually constructed databases, CellMeSH is more comprehensive and is easily updated with new data. The probabilistic query method enables reliable information retrieval even though the gene-cell type associations extracted from the literature are noisy. CellMeSH is also able to optionally utilize prior knowledge about tissues or cells for further annotation improvement. CellMeSH achieves top-one and top-three accuracies on a number of mouse and human datasets that are consistently better than existing approaches. Availability Web server at https://uncurl.cs.washington.edu/db_query and API at https://github.com/shunfumao/cellmesh.

High throughput sequencing of RNA (RNA-Seq) has become a staple in modern molecular biology, with applications not only in quantifying gene expression but also in isoform-level analysis of the RNA transcripts. To enable such an isoform-level analysis, a transcriptome assembly algorithm is utilized to stitch together the observed short reads into the corresponding transcripts. This task is complicated due to the complexity of alternative splicinga mechanism by which the same gene may generate multiple distinct RNA transcripts. We develop a novel genome-guided transcriptome assembler, RefShannon, that exploits the varying abundances of the different transcripts, in enabling an accurate reconstruction of the transcripts. Our evaluation shows RefShannon is able to improve sensitivity effectively (up to 22%) at a given specificity in comparison with other state-of-the-art assemblers. RefShannon is written in Python and is available from Github (https://github.com/ shunfumao/RefShannon).

In this paper, we propose a physical layer network coding technique combined with asynchronous code division multiple access (CDMA). We consider a scenario where there are multiple pair of nodes with a single relay, and each pair of nodes exchange information asynchronously. With the assumption of asynchronous transmission, there is a trade-off between the number of nodes and the inter-user interference because the spreading codes are not orthogonal to each other. The BER and the throughput of the system are analyzed, and simulations are performed to verify the results. It is shown that physical layer (analog) network coding improves throughput compared to the conventional routing protocol with large E b /N0, low number of users, and large spreading factor.

Analysis of single cell RNA sequencing (scRNA-Seq) datasets is a complex and time-consuming process, requiring both biological knowledge and technical skill. In order to simplify and systematize this process, we introduce UNCURL-App, an online GUI-based interactive scRNA-Seq analysis tool. UNCURL-App introduces two key innovations: First, prior knowledge in the form of cell type, anatomy, and Gene Ontology databases is integrated directly with the rest of the analysis process, allowing users to automatically map cell clusters to known cell types based on gene expression. Second, tools for interactive re-analysis allow the user to iteratively create, merge, or delete clusters in order to arrive at an optimal mapping between clusters and cell types.

Efficient and accurate alignment of DNA / RNA sequence reads to each other or to a reference genome / transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing an efficient aligner. In this paper, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome / transcriptome or to other long reads. The key idea in QAlign is to convert the nucleobase reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner. We show that QAlign improves alignment rates from around 80% to 90% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2%, 2.5% and 10.8% in three datasets for read-to-read alignment. Read to transcriptome alignment rates are improved from 50.8% to 86.3% and 82.3% to 95.3% in two datasets. to this problem by providing long reads (spanning up to 100,000 bases) that can span these repetitive regions. However, these long reads are riddled with a high error rate thus making alignment of low accuracy [7] as well as the downstream taks difficult. For example, while nanopore sequencing has enabled fully automated assembly of some bacterial genomes, the assembly of human genome still produces many contigs that then have to be scaffolded manually [8]. Another important downstream task is structural variant calling, where long reads can play an important role. However, present structural variant calling algorithms have low precision and recall due to noise in the reads [9]. The assembly of long segmental duplications presents another important problem where long reads can bridge repeated regions but again becomes complicated due to read errors [10].In this paper, we propose a novel method for aligning nanopore reads that takes into account the particular structure of errors that is inherent in the nanopore sequencing process. In many of the long read aligners, the read errors are modeled using insertions, deletions and substitutions which happen at differing rates. However, in nanopore sequencing, many errors induced have structure, which is missed by viewing the errors as independent insertions, deletions and substitutions. In the nanopore sequencer, the current level depends on a Q-mer (a set of Q bases which influence the current measurement in the nanopore). This is due to the physics of the nanopore sequencing, where a set of DNA base-pairs Percentage Aligned reads comparison K. Pneumoniae R9.4 1D E. Coli R9.4 1D E. Coli R9 2D DJ, SK, and SD conceived the original idea and developed the project. DJ led the development of the software tool. DJ and SM performed the analysis on the v...