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The probability of sequencing a set of RNA-seq reads can be directly modeled using the abundances of splice junctions in splice graphs instead of the abundances of a list of transcripts. We call this model graph quantification, which was first proposed by Bernard et al. (2014). The model can be viewed as a generalization of transcript expression quantification where every full path in the splice graph is a possible transcript. However, the previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed. We provide an improvement of this model to handle variable-length reads or fragments and incorporate bias correction. We prove that our model is equivalent to running a transcript quantifier with exactly the set of all compatible transcripts. The key to our method is constructing an extension of the splice graph based on Aho-Corasick automata. The proof of equivalence is based on a novel reparameterization of the read generation model of a state-of-art transcript quantification method. This new approach is useful for modeling scenarios where reference transcriptome is incomplete or not available and can be further used in transcriptome assembly or alternative splicing analysis.

Transcriptomic structural variants (TSVs) - large-scale transcriptome sequence change due to structural variation - are common, especially in cancer. Detecting TSVs is a challenging computational problem. Sample heterogeneity (including differences between alleles in diploid organisms) is a critical confounding factor when identifying TSVs. To improve TSV detection in heterogeneous RNA-seq samples, we introduce the Multiple Compatible Arrangement Problem (MCAP), which seeks k genome rearrangements to maximize the number of reads that are concordant with at least one rearrangement. This directly models the situation of a heterogeneous or diploid sample. We prove that MCAP is NP-hard and provide a 1/4-approximation algorithm for k=1 and a 3/4-approximation algorithm for the diploid case (k=2) assuming an oracle for k=1. Combining these, we obtain a 3/16-approximation algorithm for MCAP when k=2 (without an oracle). We also present an integer linear programming formulation for general k. We characterize the graph structures that require k>1 to satisfy all edges and show such structures are prevalent in cancer samples. We evaluate our algorithms on 381 TCGA samples and 2 cancer cell lines and show improved performance compared to the state-of-the-art TSV-calling tool, SQUID.

Magnetic anion exchange (MIEX) has been increasingly concerned owing to its excellent performance for the removal of dissolved organic carbon in the treatment of drinking water. In this study, the ability of ultrafiltration (UF) membrane fouling control was analyzed by different combination processes of MIEX-UF. A single cycle and multi-cycle operations were conducted, and the separated and integrated processes of MIEX-UF were compared. Membrane filtration resistance, removal of organics, and morphologies of the membrane surface were analyzed. The results of the short-term operation did not show any obvious improvement in the membrane fouling control in the separated process (compared to UF alone); however, a marked improvement was obtained in the integrated process. The membrane filtration resistance analysis showed that the cake resistance (Rc) and pore blocking resistance (Rp) in the integrated process were only 52 and 24% of the values in the separated process. The multi-cycle operation demonstrated that the cumulative rate of hydraulically irreversible fouling was 0.0021 in the integrated process and was much lower than that in the separated process (0.0182). The mechanistic analysis shows that in the integrated process, a dynamic layer was formed on the membrane surface by the deposited MIEX beads, which markedly decreased the accumulation of the membrane foulants on the membrane surface and in the membrane pores.