Oscillation-coordinated,noise-resistant information distribution via the subiculum

The hippocampus processes information associated with spatial navigation. The subiculum receives input from the hippocampus CA1 and projects to various cortical and subcortical regions. Thus, the subiculum is uniquely positioned to distribute hippocampal information to a range of brain areas. Subicular neurons fire at higher rates than CA1 neurons and exhibit similarly or more accurately decodable representations of place, speed, and trajectory. These representations are more noise-resistant and advantageous for long-range information transfer. Subicular neurons selectively or uniformly distribute information to target areas, depending on the information type. Theta oscillations and sharp-wave ripples control information broadcasting in a pathway-specific manner. Thus, the subiculum routes accurately decodable, noise-resistant, navigation-associated information to downstream regions.     

Sequencing antibody repertoires

Genomic studies have been revolutionized by the use of next generation sequencing (NGS) that delivers huge amounts of sequence information in a short span of time. The number of applications for NGS is rapidly expanding and significantly transforming many areas of life sciences. The field of antibody research and discovery is no exception. Several recent studies have harnessed the power of NGS for analyzing natural or synthetic immunoglobulin repertoires with unprecedented resolution and exploring alternative paths for antibody discovery. Thus, appreciating, and then exploiting, these advances is essential for staying at the edge of antibody innovation.     

Sequencing antibody repertoires: The next generation

Genomic studies have been revolutionized by the use of next generation sequencing (NGS), which delivers huge amounts of sequence information in a short span of time. The number of applications for NGS is rapidly expanding and significantly transforming many areas of life sciences. The field of antibody research and discovery is no exception. Several recent studies have harnessed the power of NGS for analyzing natural or synthetic immunoglobulin repertoires with unprecedented resolution and exploring alternative paths for antibody discovery. Thus, appreciating and then exploiting these advances is essential for staying at the edge of antibody innovation.Key words: next generation sequencing, phage display, hybridoma, antibody discovery, in vitro selection, immunization     

基于深度学习与多层次信息融合的药物靶标亲和力预测*

药物研发是非常重要但也十分耗费人力物力的过程。利用计算机辅助预测药物与蛋白质亲和力的方法可以极大地加快药物研发过程。药物靶标亲和力预测的关键在于对药物和蛋白质进行准确详细地信息表征。提出一种基于深度学习与多层次信息融合的药物靶标亲和力的预测模型,试图通过综合药物与蛋白质的多层次信息,来获得更好的预测表现。首先将药物表述成分子图和扩展连接指纹两种形式,分别利用图卷积神经网络模块和全连接层进行学习;其次将蛋白质序列和蛋白质K-mer特征分别输入卷积神经网络模块和全连接层来学习蛋白质潜在特征;随后将4个通道学习到的特征进行融合,再利用全连接层进行预测。在两个基准药物靶标亲和力数据集上验证了所提方法的有效性,并与其他已有模型作对比研究。结果说明提出的模型相比基准模型能得到更好的预测性能,表明提出的综合药物与蛋白质多层次信息的药物靶标亲和力预测策略是有效的。     

The impact of next-generation sequencing technology on genetics

If one accepts that the fundamental pursuit of genetics is to determine the genotypes that explain phenotypes, the meteoric increase of DNA sequence information applied toward that pursuit has nowhere to go but up. The recent introduction of instruments capable of producing millions of DNA sequence reads in a single run is rapidly changing the landscape of genetics, providing the ability to answer questions with heretofore unimaginable speed. These technologies will provide an inexpensive, genome-wide sequence readout as an endpoint to applications ranging from chromatin immunoprecipitation, mutation mapping and polymorphism discovery to noncoding RNA discovery. Here I survey next-generation sequencing technologies and consider how they can provide a more complete picture of how the genome shapes the organism.     

Detection of single nucleotide polymorphisms

Single nucleotide polymorphism (SNP) detection technologies are used to scan for new polymorphisms and to determine the allele(s) of a known polymorphism in target sequences. SNP detection technologies have evolved from labor intensive, time consuming, and expensive processes to some of the most highly automated, efficient, and relatively inexpensive methods. Driven by the Human Genome Project, these technologies are now maturing and robust strategies are found in both SNP discovery and genotyping areas. The nearly completed human genome sequence provides the reference against which all other sequencing data can be compared. Global SNP discovery is therefore only limited by the amount of funding available for the activity. Local, target, SNP discovery relies mostly on direct DNA sequencing or on denaturing high performance liquid chromatography (dHPLC). The number of SNP genotyping methods has exploded in recent years and many robust methods are currently available. The demand for SNP genotyping is great, however, and no one method is able to meet the needs of all studies using SNPs. Despite the considerable gains over the last decade, new approaches must be developed to lower the cost and increase the speed of SNP detection.     

Informatic tools for proteome profiling

In recent years, the practice of proteomics research has experienced a dramatic shift within the pharmaceutical and biotechnology industry with the widespread implementation of novel applications. The areas of interest extend all the way from discovery of novel drug, vaccine, and diagnostic targets, characterization of protein-based products, toxicology, and identification of surrogate markers of activity in clinical research, to the ability to provide information on the mechanisms of drug action. The power of two-dimensional gel electrophoresis as well as advances in mass spectrometric techniques combined with sequence database correlation have enabled speed and accuracy in identification of proteins in complex mixtures. This article surveys currently available software and informatic tools related to these methods for proteome profiling. The broad acceptance of these technologies, however, has not been accompanied by significant advances in the informatics and software tools necessary to support the analysis and management of the massive amounts of data generated in the process. In this context, this article also discusses the importance of relational databases for protein identification data management.     

Fast food in ant communities: how competing species find resources

An understanding of foraging behavior is crucial to understanding higher level community dynamics; in particular, there is a lack of information about how different species discover food resources. We examined the effect of forager number and forager discovery capacity on food discovery in two disparate temperate ant communities, located in Texas and Arizona. We defined forager discovery capacity as the per capita rate of resource discovery, or how quickly individual ants arrived at resources. In general, resources were discovered more quickly when more foragers were present; this was true both within communities, where species identity was ignored, as well as within species. This pattern suggests that resource discovery is a matter of random processes, with ants essentially bumping into resources at a rate mediated by their abundance. In contrast, species that were better discoverers, as defined by the proportion of resources discovered first, did not have higher numbers of mean foragers. Instead, both mean forager number and mean forager discovery capacity determined discovery success. The Texas species used both forager number and capacity, whereas the Arizona species used only forager capacity. There was a negative correlation between a species’ prevalence in the environment and the discovery capacity of its foragers, suggesting that a given species cannot exploit both high numbers and high discovery capacity as a strategy. These results highlight that while forager number is crucial to determining time to discovery at the community level and within species, individual forager characteristics influence the outcome of exploitative competition in ant communities.     

New approaches to molecular cancer therapeutics

Cancer drug development is leading the way in exploiting molecular biological and genetic information to develop "personalized" medicine. The new paradigm is to develop agents that target the precise molecular pathology driving the progression of individual cancers. Drug developers have benefited from decades of academic cancer research and from investment in genomics, genetics and automation; their success is exemplified by high-profile drugs such as Herceptin (trastuzumab), Gleevec (imatinib), Tarceva (erlotinib) and Avastin (bevacizumab). However, only 5% of cancer drugs entering clinical trials reach marketing approval. Cancer remains a high unmet medical need, and many potential cancer targets remain undrugged. In this review we assess the status of the discovery and development of small-molecule cancer therapeutics. We show how chemical biology approaches offer techniques for interconnecting elements of the traditional linear progression from gene to drug, thereby providing a basis for increasing speed and success in cancer drug discovery.     

Babesiosis: recent insights into an ancient disease

Ever since the discovery of parasitic inclusions in erythrocytes of cattle in Romania by Victor Babes at the end of the 19th century, newly recognised babesial pathogens continue to emerge around the world and the substantial public health impact of babesiosis on livestock and man is ongoing. Babesia are transmitted by ixodid ticks and infection of the host causes a host-mediated pathology and erythrocyte lysis, resulting in anemia, hyperbilirubinuria, hemoglobinuria, and possibly organ failure. Recently obtained molecular data, particularly for the 18S rRNA gene, has contributed significantly to a better understanding of the sometimes puzzling phylogenetic situation of the genus Babesia and new information has been added to help determine the taxonomic position of many species. Moreover, it seems that owing to higher medical awareness the number of reported cases in humans is rising steadily. Hitherto unknown zoonotic babesias are now being reported from geographical areas where babesiosis was not known to occur and the growing numbers of immunocompromised individuals suggest that the frequency of cases will continue to rise. This review covers recent insights into human babesiosis with regard to phylogeny, diagnostics and treatment in order to provide new information on well known as well as recently discovered parasites with zoonotic potential.     

High-throughput screening: update on practices and success

High-throughput screening (HTS) has become an important part of drug discovery at most pharmaceutical and many biotechnology companies worldwide, and use of HTS technologies is expanding into new areas. Target validation, assay development, secondary screening, ADME/Tox, and lead optimization are among the areas in which there is an increasing use of HTS technologies. It is becoming fully integrated within drug discovery, both upstream and downstream, which includes increasing use of cell-based assays and high-content screening (HCS) technologies to achieve more physiologically relevant results and to find higher quality leads. In addition, HTS laboratories are continually evaluating new technologies as they struggle to increase their success rate for finding drug candidates. The material in this article is based on a 900-page HTS industry report involving 54 HTS directors representing 58 HTS laboratories and 34 suppliers.     

Integrated bio-entity network: a system for biological knowledge discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein-protein interactions, protein/gene regulations, protein-small molecule interactions, protein-GO relationships, protein-pathway relationships, and pathway-disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses--the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs.     

Speeding up the Consensus Clustering methodology for microarray data analysis

Background

Position-specific priors (PSP) have been used with success to boost EM and Gibbs sampler-based motif discovery algorithms. PSP information has been computed from different sources, including orthologous conservation, DNA duplex stability, and nucleosome positioning. The use of prior information has not yet been used in the context of combinatorial algorithms. Moreover, priors have been used only independently, and the gain of combining priors from different sources has not yet been studied.

Results

We extend RISOTTO, a combinatorial algorithm for motif discovery, by post-processing its output with a greedy procedure that uses prior information. PSP's from different sources are combined into a scoring criterion that guides the greedy search procedure. The resulting method, called GRISOTTO, was evaluated over 156 yeast TF ChIP-chip sequence-sets commonly used to benchmark prior-based motif discovery algorithms. Results show that GRISOTTO is at least as accurate as other twelve state-of-the-art approaches for the same task, even without combining priors. Furthermore, by considering combined priors, GRISOTTO is considerably more accurate than the state-of-the-art approaches for the same task. We also show that PSP's improve GRISOTTO ability to retrieve motifs from mouse ChiP-seq data, indicating that the proposed algorithm can be applied to data from a different technology and for a higher eukaryote.

Conclusions

The conclusions of this work are twofold. First, post-processing the output of combinatorial algorithms by incorporating prior information leads to a very efficient and effective motif discovery method. Second, combining priors from different sources is even more beneficial than considering them separately.     

Faster migration in autumn than in spring: seasonal migration patterns and non‐breeding distribution of Icelandic whimbrels Numenius phaeopus islandicus

Migration is fundamental in the life of many birds and entails significant energetic and time investments. Given the importance of arrival time in the breeding area and the relatively short period available to reproduce (particularly at high latitudes), it is expected that birds reduce spring migration duration to a greater extent than autumn migration, assuming that pressure to arrive into the wintering area might be relaxed. This has previously been shown for several avian groups, but recent evidence from four tracked Icelandic whimbrels Numenius phaeopus islandicus, a long distance migratory wader, suggests that this subspecies tends to migrate faster in autumn than in spring. Here, we 1) investigate differences in seasonal migration duration, migration speed and ground speed of whimbrels using 56 migrations from 19 individuals tracked with geolocators and 2) map the migration routes, wintering and stopover areas for this population. Tracking methods only provide temporal information on the migration period between departure and arrival. However, migration starts with the fuelling that takes place ahead of departure. Here we estimate the period of first fuelling using published fuel deposition rates and thus explore migration speed using tracking data. We found that migration duration was shorter in autumn than in spring. Migration speed was higher in autumn, with all individuals undertaking a direct flight to the wintering areas, while in spring most made a stopover. Wind patterns could drive whimbrels to stop in spring, but be more favourable during autumn migration and allow a direct flight. Additionally, the stopover might allow the appraisal of weather conditions closer to the breeding areas and/or improve body condition in order to arrive at the breeding sites with reserves.     

Using genomic databases for sequence-based biological discovery

The inherent potential underlying the sequence data produced by the International Human Genome Sequencing Consortium and other systematic sequencing projects is, obviously, tremendous. As such, it becomes increasingly important that all biologists have the ability to navigate through and cull important information from key publicly available databases. The continued rapid rise in available sequence information, particularly as model organism data is generated at breakneck speed, also underscores the necessity for all biologists to learn how to effectively make their way through the expanding "sequence information space." This review discusses some of the more commonly used tools for sequence discovery; tools have been developed for the effective and efficient mining of sequence information. These include LocusLink, which provides a gene-centric view of sequence-based information, as well as the 3 major genome browsers: the National Center for Biotechnology Information Map Viewer, the University of California Santa Cruz Genome Browser, and the European Bioinformatics Institute's Ensembl system. An overview of the types of information available through each of these front-ends is given, as well as information on tutorials and other documentation intended to increase the reader's familiarity with these tools.     

A survey of motif discovery methods in an integrated framework

Background  

There has been a growing interest in computational discovery of regulatory elements, and a multitude of motif discovery methods have been proposed. Computational motif discovery has been used with some success in simple organisms like yeast. However, as we move to higher organisms with more complex genomes, more sensitive methods are needed. Several recent methods try to integrate additional sources of information, including microarray experiments (gene expression and ChlP-chip). There is also a growing awareness that regulatory elements work in combination, and that this combinatorial behavior must be modeled for successful motif discovery. However, the multitude of methods and approaches makes it difficult to get a good understanding of the current status of the field.     

Monitoring and Control of Bioproducts from Conception to Production in Real‐time Using an Optical Biosensor

The ability to observe biological interactions in real‐time using optical biosensor technology provides the scientist/engineer with a valuable analytical tool to analyze biological molecules. Production of biological products is a growing area, but the course of discovery through to production is lengthy and complex, especially for therapeutic products. However, the economics of developing new products are clear, time to market for a new product is the primary consideration. Limited patent lifetimes, the need to get a return on the investment in research and gain competitive advantage by launching products before competitors all contribute to the necessity to get products to market. The ability of biosensor technology to give rapid, direct information on key biological parameters, (such as product concentration), makes it a suitable analytical tool to help accelerate the development of biological products. It is difficult to conceive how direct measurements of this speed and selectivity can be possible with other analytical methods. This paper aims to describe how this potential can be brought to use from biological product conception and discovery all the way through to process operation and quality control.     

Practical points in urinary proteomics

During the proteomic era, one of the most rapidly growing areas in biomedical research is biomarker discovery, particularly using proteomic technologies. Urinary proteomics has become one of the most attractive subdisciplines in clinical proteomics, as the urine is an ideal source for the discovery of noninvasive biomarkers for human diseases. However, there are several barriers to the success of the field and urinary proteome analysis is not a simple task because the urine has low protein concentration, high levels of salts or other interfering compounds, and more importantly, high degree of variations (both intra-individual and inter-individual variabilities). This article provides step-by-step practical points to perform urinary proteome analysis, covering detailed information for study design, sample collection, sample storage, sample preparation, proteomic analysis, and data interpretation. The discussion herein should stimulate further discussion and refinement to develop guidelines and standardizations for urinary proteome study.     

Ligand binding: functional site location,similarity and docking

Computational methods for the detection and characterisation of protein ligand-binding sites have increasingly become an area of interest now that large amounts of protein structural information are becoming available prior to any knowledge of protein function. There have been particularly interesting recent developments in the following areas: first, functional site detection, whereby protein evolutionary information has been used to locate binding sites on the protein surface; second, functional site similarity, whereby structural similarity and three-dimensional templates can be used to compare and classify and potentially locate new binding sites; and third, ligand docking, which is being used to find and validate functional sites, in addition to having more conventional uses in small-molecule lead discovery.