タンパク質の機能予測のための深層転移学習法に関する研究

早大学位記番号:新9098早稲田大

Automatic discovery of cross-family sequence features associated with protein function

BACKGROUND: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. RESULTS: We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription" function than to the general "nuclear" function/location. CONCLUSION: We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription

Poly(A) Tail Regulation in the Nucleus

Der Ribonukleinsäure (RNS) Stoffwechsel umfasst verschiedene Schritte, beginnend mit der Transkription der RNS über die Translation bis zum RNA Abbau. Poly(A) Schwänze befinden sich am Ende der meisten der Boten-RNS, schützen die RNA vor Abbau und stimulieren Translation. Die Deadenylierung von Poly(A) Schwänzen limitiert den Abbau von RNS. Bisher wurde RNS Abbau meist im Kontext von cytoplasmatischen Prozessen untersucht, ob und wie RNS Deadenylierung und Abbau in Nukleus erfolgen ist bisher unklar. Es wurde daher eine neue Methode zur genomweiten Bestimmung von Poly(A) Schwanzlänge entwickelt, welche FLAM-Seq genannt wurde. FLAM-Seq wurde verwendet um Zelllinien, Organoide und C. elegans RNS zu analysieren und es wurde eine signifikante Korrelation zwischen 3’-UTR und Poly(A) Länge gefunden, sowie für viele Gene ein Zusammenhang von alternativen 3‘-UTR Isoformen und Poly(A) Länge. Die Untersuchung von Poly(A) Schwänzen von nicht-gespleißten RNS Molekülen zeige, dass deren Poly(A) Schwänze eine Länge von mehr als 200 nt hatten. Die Analyse wurde durch eine Inhibition des Spleiß-Prozesses validiert. Die Verwendung von Methoden zur Markierung von RNS, welche die zeitliche Auflösung der RNS Prozessierung ermöglicht, deutete auf eine Deadenylierung der Poly(A) Schwänze schon wenige Minuten nach deren Synthesis hin. Die Analyse von subzellulären Fraktionen zeigte, dass diese initiale Deadenylierung ein Prozess im Nukleus ist. Dieser Prozess ist gen-spezifisch und Poly(A) Schwänze von bestimmten Typen von Transkripten, wie nuklearen langen nicht-kodierende RNS Molekülen waren nicht deadenyliert. Um Enzyme zu identifizieren, welche die Deadenylierung im Zellkern katalysieren, wurden verschiedene Methoden wie RNS-abbauende Cas Systeme, siRNAs oder shRNA Zelllinien verwendet. Trotz einer effizienten Reduktion der RNS Expression entsprechender Enzymkomplexe konnten keine molekularen Phänotypen identifiziert werden welche die Poly(A) Länge im Zellkern beeinflussen.The RNA metabolism involves different steps from transcription to translation and decay of messenger RNAs (mRNAs). Most mRNAs have a poly(A) tail attached to their 3’-end, which protects them from degradation and stimulates translation. Removal of the poly(A) tail is the rate-limiting step in RNA decay controlling stability and translation. It is yet unclear if and to what extent RNA deadenylation occurs in the mammalian nucleus. A novel method for genome-wide determination of poly(A) tail length, termed FLAM-Seq, was developed to overcome current challenges in sequencing mRNAs, enabling genome-wide analysis of complete RNAs, including their poly(A) tail sequence. FLAM-Seq analysis of different model systems uncovered a strong correlation between poly(A) tail and 3’-UTR length or alternative polyadenylation. Cytosine nucleotides were further significantly enriched in poly(A) tails. Analyzing poly(A) tails of unspliced RNAs from FLAM-Seq data revealed the genome-wide synthesis of poly(A) tails with a length of more than 200 nt. This could be validated by splicing inhibition experiments which uncovered potential links between the completion of splicing and poly(A) tail shortening. Measuring RNA deadenylation kinetics using metabolic labeling experiments hinted at a rapid shortening of tails within minutes. The analysis of subcellular fractions obtained from HeLa cells and a mouse brain showed that initial deadenylation is a nuclear process. Nuclear deadenylation is gene specific and poly(A) tails of lncRNAs retained in the nucleus were not shortened. To identify enzymes responsible for nuclear deadenylation, RNA targeting Cas-systems, siRNAs and shRNA cell lines were used to different deadenylase complexes. Despite efficient mRNA knockdown, subcellular analysis of poly(A) tail length by did not yield molecular phenotypes of changing nuclear poly(A) tail length

The Chlamydomonas genome project: A decade on

The green alga Chlamydomonas reinhardtii is a popular unicellular organism for studying photosynthesis, cilia biogenesis, and micronutrient homeostasis. Ten years since its genome project was initiated an iterative process of improvements to the genome and gene predictions has propelled this organism to the forefront of the omics era. Housed at Phytozome, the plant genomics portal of the Joint Genome Institute (JGI), the most up-to-date genomic data include a genome arranged on chromosomes and high-quality gene models with alternative splice forms supported by an abundance of whole transcriptome sequencing (RNA-Seq) data. We present here the past, present, and future of Chlamydomonas genomics. Specifically, we detail progress on genome assembly and gene model refinement, discuss resources for gene annotations, functional predictions, and locus ID mapping between versions and, importantly, outline a standardized framework for naming genes

Systems analysis of host-parasite interactions.

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug-resistant parasites necessitates that the research community take an active role in understanding host-parasite infection biology in order to develop improved therapeutics. Recent advances in next-generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host-parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high-throughput -omic data will undoubtedly generate extraordinary insight into host-parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host-parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies

Deep Learning for Genomics: A Concise Overview

Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning Application

ASPicDB: a database of annotated transcript and protein variants generated by alternative splicing

Alternative splicing is emerging as a major mechanism for the expansion of the transcriptome and proteome diversity, particularly in human and other vertebrates. However, the proportion of alternative transcripts and proteins actually endowed with functional activity is currently highly debated. We present here a new release of ASPicDB which now provides a unique annotation resource of human protein variants generated by alternative splicing. A total of 256 939 protein variants from 17 191 multi-exon genes have been extensively annotated through state of the art machine learning tools providing information of the protein type (globular and transmembrane), localization, presence of PFAM domains, signal peptides, GPI-anchor propeptides, transmembrane and coiled-coil segments. Furthermore, full-length variants can be now specifically selected based on the annotation of CAGE-tags and polyA signal and/or polyA sites, marking transcription initiation and termination sites, respectively. The retrieval can be carried out at gene, transcript, exon, protein or splice site level allowing the selection of data sets fulfilling one or more features settled by the user. The retrieval interface also enables the selection of protein variants showing specific differences in the annotated features. ASPicDB is available at http://www.caspur.it/ASPicDB/