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The open-source DIAMOND software provides protein alignment that is 20,000 times faster on short reads than BLASTX at similar sensitivity, for rapid analysis of large metagenomics data sets on a desktop computer. The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.

Online genealogies are promising data sources for demographic research, but their limitations are understudied. This paper takes a critical approach to evaluating the potential strengths and weaknesses of using online genealogical data for population studies. We focus on the FamiLinx dataset, which contains demographic information and kinship ties across multiple countries and centuries. We propose novel measures to assess the completeness and the quality of demographic variables in the FamiLinx data at both the individual and the familial level over the 16001900 period. Utilizing Sweden as a test country, we investigate how the age-sex distribution and the mortality levels of the digital population extracted from FamiLinx diverge from the registered population. We employ descriptive statistics, negative binomial regression modeling, and standard life table techniques for our measures of completeness and quality. Missing values and accuracy in demographic information from FamiLinx are selective. When one demographic variable is available, researchers can effectively anticipate the availability of other demographic information. The completeness and quality of demographic variables within kinship networks are markedly higher for individuals with more complete and accurate demographic information. Populations from FamiLinx display lower mortality levels than the registered population and their representativeness improves towards the end of the 19th century.

Background Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. Description BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database. Conclusions BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License.

[...]familial DNA searching allows law enforcement agencies to take unknown DNA from crime scenes and find potential family members by looking for hereditary markers. [...]this paper will address criticisms of finding familial DNA searching constitutional. CODIS and NDIS All of the DNA indexes are under the umbrella of the FBI's Combined DNA Index System (“CODIS”).37 CODIS began as a pilot software project in 1990 and was officially formalized under the DNA Identification Act.38 The Act also gave the FBI authority to establish the National DNA Index System (“NDIS”) for law enforcement purposes.39 As of today, almost 200 public law enforcement agencies in the United States participate in NDIS, and more than 90 law enforcement laboratories in over 50 countries utilize CODIS software.40 NDIS contains the DNA data from convicted offenders, arrestees, detainees, forensic casework, unidentified human remains, and relatives of missing persons, collected by federal, state, and local participating laboratories.41 NDIS accepts DNA data from the three main types of tests: PCR STR, Y STR, and mtDNA.42 Each test has different requirements for submission to NDIS.43 For PCR STR and Y STR, laboratories must test for the twenty “CODIS Core Loci.” 44 For mtDNA, laboratories must test from two separate regions within the mitochondria.45 To submit DNA to NDIS, several requirements must be met: (1) the DNA data must be generated in accordance with the FBI Director's Quality Assurance Standards; (2) the DNA data must be generated by a laboratory that is accredited by an approved accrediting agency; (3) the DNA data must be generated by a laboratory that undergoes an external audit every two years to demonstrate compliance with the FBI Director's Quality Assurance Standards; (4) the DNA data must be one of the categories of data acceptable at NDIS, such as convicted offender, arrestee, detainee, legal, forensic (casework), unidentified human remains, missing person, or a relative of missing person; (5) the DNA data must meet minimum CODIS Core Loci requirements for the specimen category; (6) the DNA PCR data must be generated using PCR accepted kits; and (7) participating laboratories must have and follow expungement procedures in accordance with federal law.46 Laboratories who wish to participate in NDIS must sign a Memorandum of Understanding, indicating their agreement to abide by these requirements.47 If a laboratory fails to comply with these standards, it may lose its ability to participate in the system.48 2.

Parabon Nanolabs shot to fame using DNA and genealogy analysis to solve cold cases. Then it hit a setback. Parabon Nanolabs shot to fame using DNA and genealogy analysis to solve cold cases. Then it hit a setback.

Introduction It is now becoming clear that protein interactions determine the outcome of most cellular processes [1-4]. [...]identifying and characterizing protein-protein interactions and their networks is essential for understanding the mechanisms of biological processes on a molecular level. [...]the CBM database can be used to categorize the specific interaction surfaces that have evolved from conserved domains and thereby allows for the homology modeling of protein interaction interfaces.

Investigative genetic genealogy (IGG) is a new technique for identifying criminal suspects that has sparked controversy. The technique involves uploading a crime scene DNA profile to one or more genetic genealogy databases with the intention of identifying a criminal offender’s genetic relatives and, eventually, locating the offender within the family tree. IGG was used to identify the Golden State Killer in 2018 and it is now being used in connection with hundreds of cases in the USA. Yet, as more law enforcement agencies conduct IGG, the privacy implications of the technique have come under scrutiny. While these issues deserve careful attention, we are concerned that their discussion is, at times, based on misunderstandings related to how IGG is used in criminal investigations and how IGG departs from traditional investigative techniques. Here, we aim to clarify and sharpen the public debate by addressing four misconceptions about IGG. We begin with a detailed description of IGG as it is currently practiced: what it is and—just as important—what it is not. We then examine misunderstood or not widely known aspects of IGG that are potentially confusing efforts to have constructive discussions about its future. We conclude with recommendations intended to support the productivity of those discussions.

The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content.

In recent years, DNA has become increasingly easy to collect, test, and sequence, making it far more accessible to law enforcement. While legal scholars have examined this phenomenon generally, this Article examines the control and use of children's DNA, asking who ultimately owns children's DNA. I explore two common ways parents-currently considered \"owners\" of children's DNA- might turn over children's DNA to law enforcement: (1) \"consensual\" searches and (2) direct-to-consumertesting. My fundamental thesis is that parentalconsent is an insufficient safeguard to protect a child's DNA from law enforcement. At present, the law leaves parents in complete control of children's DNA, with parents' and children's interests viewed as totally unified. This Article is the first to argue that parents might have serious conflicts of interest and even encourage (or be the ones) sharing DNA with law enforcement. This Article contributes to the literature by using the functional logic of a property rights framework, including property law's rare virtue of historically recognizing children's and parents' interests as separate. A property-like interest in one's DNA leads to solutions that create safeguards beyond parental consent. Ultimately, I advocate for moving from a framework of parents-as-owners to parents-as-fiduciaries-of both children's DNA and of children themselves.

Human microbiome reference datasets provide epidemiological context for researchers, enabling them to uncover new insights into their own data through meta-analyses. In addition, large and comprehensive reference sets offer a means to develop or test hypotheses and can pave the way for addressing practical study design considerations such as sample size decisions. We discuss the importance of reference sets in human microbiome research, limitations of existing resources, technical challenges to employing reference sets, examples of their usage, and contributions of the American Gut Project to the development of a comprehensive reference set. Through engaging the general public, the American Gut Project aims to address many of the issues present in existing reference resources, characterizing health and disease, lifestyle, and dietary choices of the participants while extending its efforts globally through international collaborations.