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Transposable elements are powerful mutagens. Along with genomic sequences, knock-out phenotypes and expression patterns are important information to elucidate the function of genes. In this review, I propose a strategy to develop tranposant lines on a large scale by combining genetic cross and tissue culture of Ac and Ds lines. Based on the facts that Ds tends to be inactive in F2 or later generation and Ds becomes reactivated via tissue culture, a large scale of transposants can be produced by tissue culture of seeds carrying Ac and inactive Ds. In this review, I describe limitations and considerations in operating transposon tagging systems in rice. Also, I discuss the efficiency of our gene trap system and technical procedures to clone Ds flanking DNA.

A coordinated international effort to sequence agricultural and livestock genomes has come to its time. While human genome and genomes of many model organisms (related to human health and basic biological interests) have been sequenced or plugged in the sequencing pipelines, agronomically important crop and livestock genomes have not been given high enough priority.

Although we are facing many challenges in policy-making, grant funding, regional task emphasis, research community consensus and technology innovations, many initiatives are being announced and formulated based on the cost-effective and large-scale sequencing procedure, known as whole genome shotgun (WGS) sequencing that produces draft sequences covering a genome from 95 percent to 99 percent.

Identified genes from such draft sequences, coupled with other resources, such as molecular markers, large-insert clones and cDNA sequences, provide ample information and tools to further our knowledge in agricultural and environmental biology in the genome era that just comes to its accelerated period.

If the campaign succeeds, molecular biologists, geneticists and field biologists from all countries, rich or poor, would be brought to the same starting point and expect another astronomical increase of basic genomic information, ready to convert effectively into knowledge that will ultimately change our lives and environment into a greater and better future. We call upon national and international governmental agencies and organizations as well as research foundations to support this unprecedented movement.

Visionaries Who Inspire — 16 Outstanding Researchers and Young Talents Receive Singapore's Highest Honor in Science and Technology.

Rice Functional Genomics in Singapore.

THEMATICS (Theoretical Microscopic Titration Curves) is a simple, reliable computational predictor of the active sites of enzymes from structure. Our method, based on well-established Finite Difference Poisson–Boltzmann techniques, identifies the ionisable residues with anomalous predicted titration behavior. A cluster of two or more such perturbed residues is a very reliable predictor of the active site. The protein does not have to bear any resemblance in sequence or structure to any previously characterized protein, but the method does require the three-dimensional structure. We now present evidence that THEMATICS can also locate the active site in structures built by comparative modeling from similar structures. Results are given for a total of 21 sets of proteins, including 21 templates and 83 comparative model structures. Detailed results are presented for three sets of orthologous proteins (Triosephosphate isomerase, 6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase, and Aspartate aminotransferase) and for one set of human homologues of Aldose reductase with different functions. THEMATICS correctly locates the active site in the model structures. This suggests that the method can be applicable to a much larger set of proteins for which an experimentally determined structure is unavailable. With a few exceptions, the predicted active sites in the comparative model structures are similar to that of the corresponding template structure.

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.

Gene regulation in eukaryotes involves a complex interplay between the proximal promoter and distal genomic elements (such as enhancers) which work in concert to drive precise spatio-temporal gene expression. The experimental localization and characterization of gene regulatory elements is a very complex and resource-intensive process. The computational identification of regulatory regions that confer spatiotemporally specific tissue-restricted expression of a gene is thus an important challenge for computational biology. One of the most popular strategies for enhancer localization from DNA sequence is the use of conservation-based prefiltering and more recently, the use of canonical (transcription factor motifs) or de novo tissue-specific sequence motifs. However, there is an ongoing effort in the computational biology community to further improve the fidelity of enhancer predictions from sequence data by integrating other, complementary genomic modalities.

In this work, we propose a framework that complements existing methodologies for prospective enhancer identification. The methods in this work are derived from two key insights: (i) that chromatin modification signatures can discriminate proximal and distally located regulatory regions and (ii) the notion of promoter-enhancer cross-talk (as assayed in 3C/5C experiments) might have implications in the search for regulatory sequences that co-operate with the promoter to yield tissue-restricted, gene-specific expression.

Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene multifunctionality based on functional annotations of the target gene from the Gene Ontology. The method is based on identifying pairs of GO annotations that represent semantically different biological functions and any gene annotated with two annotations from one pair is considered multifunctional. The proposed method can be employed to identify multifunctional genes in the entire human genome using solely the GO annotations. We evaluated the proposed method in scoring multifunctionality of all human genes using four criteria: gene-disease associations; protein–protein interactions; gene studies with PubMed publications; and published known multifunctional gene sets. The evaluation results confirm the validity and reliability of the proposed method for identifying multifunctional human genes. The results across all four evaluation criteria were statistically significant in determining multifunctionality. For example, the method confirmed that multifunctional genes tend to be associated with diseases more than other genes, with significance $p<0.01$. Moreover, consistent with all previous studies, proteins encoded by multifunctional genes, based on our method, are involved in protein–protein interactions significantly more ($p<0.01$) than other proteins.

Coral reefs are home to over 2 million species and provide habitat for roughly 25% of all marine animals, but they are being severely threatened by pollution and climate change. A large amount of genomic, transcriptomic and other -omics data from different species of reef building corals, the uni-cellular dinoagellates, plus the coral microbiome (where corals have possibly the most complex microbiome yet discovered, consisting of over 20,000 different species), is becoming increasingly available for corals. This new data present an opportunity for bioinformatics researchers and computational biologists to contribute to a timely, compelling, and urgent investigation of critical factors that influence reef health and resilience. This paper summarizes the content of the Bioinformatics of Corals workshop, that is being held as part of PSB 2021. It is particularly relevant for this workshop to occur at PSB, given the abundance of and reliance on coral reefs in Hawaii and the conference’s traditional association with the region.

Software has provided cell biologists the power to quantify specific cellular features in cell images at scale. Before long, these biologists also recognized the potential to extract much more biological information from the same images. From here, the field of image-based profiling, the process of extracting unbiased representations that capture morphological cell state, was born. We are still in the early days of image-based profiling, and it is clear that the many opportunities to interrogate biological systems come with significant challenges. These challenges include building expressive and biologically-relevant representations, adjusting for technical noise, writing generalizable software infrastructure, continuing to foster a culture of open science, and promoting FAIR (findable, accessible, interoperable, and reusable) data. We present a workshop at the Pacific Symposium on Biocomputing 2022 to introduce the field of image-based profiling to the broader computational biology community. In the following document, we introduce image-based profiling, discuss current state-of-the-art methods and limitations, and provide rationale for why now is the perfect time for the field to expand. We also introduce our invited speakers and agenda, which together provide an introduction to the field complemented by in-depth application areas in industry and academia. We also include five lightning talks to complement the invited speakers on various methodological and discovery advances.

Deciphering the genetic and molecular mechanisms controlling the development of the root system and its adaptive plasticity under adverse environments is of primary importance for the sustainable establishment of the rice crop. Rice displays a complex root structure comprising several root types mostly of postembryonic origin. The large natural variation in root architecture among cultivars reflects their adaptation to contrasting agro-environmental conditions. This article reviews the current knowledge on the organization and anatomy of the various types of roots of the fibrous root system of rice, the diversity and genetic basis of natural variation of root system architecture and performance, and the molecular mechanisms underlying constitutive and adaptive root development. This paper also throws light on how the integrated approach of new tools in high-resolution microscopy imaging, expression profiling, mutant screening, and reverse genetics could facilitate the rapid discovery and analysis of the key genes and regulatory networks involved in root architectural traits affecting plant performance under field conditions.

The rice genome sequenced and annotated by the IRGSP has identified 37,544 protein-coding genes. In an effort to identify genes encoding transcription factors and signal transduction components, more than 7,000 genes belonging to 87 classes have been used to prepare a local database. Detailed analysis of genes for plant hormone response, CDPKs, C₂H₂ zinc-finger, and SET domain proteins unraveled interesting evolutionary aspects in relation to genes and the rice genome. A 51k microarray, SAGE analysis, and real-time polymerase chain reaction revealed differential expression of target genes during reproductive development and stress conditions. Several genes specific to reproductive floral organs and seed development have been identified. A large number of SAGE tags are observed from intergenic regions and antisense strands reflecting the unexplored transcription potential of the rice genome. Analysis of rice gene promoter activities has been undertaken in transgenic tobacco/Arabidopsis to demarcate regions conferring anther-/pollen-specific expression. OSISAP1, a gene coding for a stress-associated zinc-finger protein, and its promoter have been functionally validated in transgenic tobacco and rice. Genes for proteins interacting with OSISAP1 have also been found to be stress-inducible. Investigations on functional analysis of stress-responsive genes are in progress.

Big data bring new opportunities for methods that efficiently summarize and automatically extract knowledge from such compendia. While both supervised learning algorithms and unsupervised clustering algorithms have been successfully applied to biological data, they are either dependent on known biology or limited to discerning the most significant signals in the data. Here we present denoising autoencoders (DAs), which employ a data-defined learning objective independent of known biology, as a method to identify and extract complex patterns from genomic data. We evaluate the performance of DAs by applying them to a large collection of breast cancer gene expression data. Results show that DAs successfully construct features that contain both clinical and molecular information. There are features that represent tumor or normal samples, estrogen receptor (ER) status, and molecular subtypes. Features constructed by the autoencoder generalize to an independent dataset collected using a distinct experimental platform. By integrating data from ENCODE for feature interpretation, we discover a feature representing ER status through association with key transcription factors in breast cancer. We also identify a feature highly predictive of patient survival and it is enriched by FOXM1 signaling pathway. The features constructed by DAs are often bimodally distributed with one peak near zero and another near one, which facilitates discretization. In summary, we demonstrate that DAs effectively extract key biological principles from gene expression data and summarize them into constructed features with convenient properties.