Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension

In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of the TQA dataset. First, solving the TQA problems requires to comprehend multi-modal contexts in complicated input data. To tackle this issue of extracting knowledge features from long text lessons and merging them with visual features, we establish a context graph from texts and images, and propose a new module f-GCN based on graph convolutional networks (GCN). Second, scientific terms are not spread over the chapters and subjects are split in the TQA dataset. To overcome this so called "out-of-domain" issue, before learning QA problems, we introduce a novel self-supervised open-set learning process without any annotations. The experimental results show that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems.Comment: ACL2019 Camera-read

DIG-seq: a genome-wide CRISPR off-target profiling method using chromatin DNA

To investigate whether and how CRISPR-Cas9 on-target and off-target activities are affected by chromatin in eukaryotic cells, we first identified a series of identical endogenous DNA sequences present in both open and closed chromatin regions and then measured mutation frequencies at these sites in human cells using Cas9 complexed with matched or mismatched sgRNAs. Unlike matched sgRNAs, mismatched sgRNAs were highly sensitive to chromatin states, suggesting that off-target but not on-target DNA cleavage is hindered by chromatin. We next performed Digenome-seq using cell-free chromatin DNA (now termed DIG-seq) and histone-free genomic DNA in parallel and found that only a subset of sites, cleaved in histone-free DNA, were cut in chromatin DNA, suggesting that chromatin can inhibit Cas9 off-target effects in favor of its genome-wide specificity in cells.

Dynamic Graph Generation Network: Generating Relational Knowledge from Diagrams

In this work, we introduce a new algorithm for analyzing a diagram, which contains visual and textual information in an abstract and integrated way. Whereas diagrams contain richer information compared with individual image-based or language-based data, proper solutions for automatically understanding them have not been proposed due to their innate characteristics of multi-modality and arbitrariness of layouts. To tackle this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with activation of gates in gated recurrent unit (GRU) cells. On publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering shows potentials of the proposed method for various applications

Genome-wide CRISPR/Cas9 off-target profiling via Digenome-seq

학위논문 (박사)-- 서울대학교 대학원 : 화학부 생화학전공, 2016. 8. 이연.Targeted genome editing using engineered nuclease such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR associated protein (Cas) systems have been used in cultured cells and whole organisms for functional study and therapeutic study. Despite broad interest in CRISPR/Cas9 mediated genomengineering, off-target effects of entire genome have not been established. Therefore, development of methods to profiling genome-wide CRISPR/Cas9 off-target effects is the major challenge in this area. In this study, I characterize CRISPR/Cas9 off-target effect in clonal cells and bulk populations of cells. First I used Isaac variant calling program to analyze genome-wide indels in clonal cells. Second, I developed nuclease-digested genomes sequencing (digenome-seq) to profiling genome-wide CRISPR/Cas9 off-target effects in bulk populations. Using this methods, I validated off-target sites which indels were induced with frequencies below 0.1% and validated off-target effects can be avoided by replacing with modified sgRNAs. Third, I developed multiplex digenome-seq which can profiling more than ten sgRNA off-target effects in a one time. Based on multiplex digenome-seq result, I made a program for the choice of target sites and the off-target sites predictor respectively.I. Introduction 1 II. Materials and Methods 6 1. Cas9 and in vitro sgRNA 6 2. Cell culture and transfection conditions 6 3. In vitro cleavage of genomic DNA 7 4. T7E1 assay 8 5. Targeted deep sequencing 8 6. Whole genome and digenome sequencing 8 7. Analysis of off-target effects at homologous sites 9 III. Results 10 A. Off-target analysis of clonal cells using whole genome sequencing (WGS) 10 1. Generation of mutant human haploid cells 10 2. Whole genome sequencing of human haploid cells 12 3. Examining potential off-target sites. 18 B. Digenome-seq for genome-wide RGEN off-target profiling 21 1. Genomic DNA digestion using RGENs in vitro 21 2. Nuclease-digested genomes sequencing (Digenome –seq) : Straight alignment vs. staggered alignment 25 3. 5 End plot at single nucleotide resolution 29 4. Deep sequencing to confirm off-target effects at candidate sites 38 5. Digenome sequencing with another promiscuous RGEN 41 6. Avoiding RGEN off-target effects via modified sgRNAs 48 C. Multiplex Digenome-seq for genome-wide target specificities of RGEN 52 1. Improving Digenome-seq 52 2. Multiplex Digenome-seq 59 3. In vitro cleavage sites 63 4. Digenome-seq vs. other methods 68 5. Validation of off-target sites in cells 75 D. Generation of RGEN targetable sites prediction program 82 E. Generation of RGEN potential off-target sites prediction program based on Digenome-seq 87 1. In vitro cleavage of genomic DNA 87 2. Generation of RGEN potential off-target sites prediction program 89 IV. Discussion 92 V. Reference 96 Abstract in Korean 104Docto

Genotyping with CRISPR-Cas-derived RNA-guided endonucleases

Restriction fragment length polymorphism (RFLP) analysis is one of the oldest, most convenient and least expensive methods of genotyping, but is limited by the availability of restriction endonuclease sites. Here we present a novel method of employing CRISPR/Cas-derived RNA-guided engineered nucleases (RGENs) in RFLP analysis. We prepare RGENs by complexing recombinant Cas9 protein derived from Streptococcus pyogenes with in vitro transcribed guide RNAs that are complementary to the DNA sequences of interest. Then, we genotype recurrent mutations found in cancer and small insertions or deletions (indels) induced in cultured cells and animals by RGENs and other engineered nucleases such as transcription activator-like effector nucleases (TALENs). Unlike T7 endonuclease I or Surveyor assays that are widely used for genotyping engineered nuclease-induced mutations, RGEN-mediated RFLP analysis can detect homozygous mutant clones that contain identical biallelic indel sequences and is not limited by sequence polymorphisms near the nuclease target sites.