ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data

Tobacco cultivation as a driver of land use change and degradation in the miombo woodlands of south‐west Tanzania

Miombo woodlands support agriculture, biodiversity, and multiple ecosystem services across an extensive part of sub‐Saharan Africa. Miombo is frequently overutilised with deforestation and degradation resulting in significant land use and land cover change (LULCC). Understanding the drivers of LULCC is essential to achieving sustainable land management in miombo woodland regions. Within a remote miombo area of south‐west Tanzania in the Kipembawe Division, Mbeya Region, social survey and ecological data were used to identify the direct and indirect drivers of LULCC. Our findings show that tobacco (Nicotiana tabacum) production results in an estimated annual deforestation rate of 4,134 ± 390 ha of undisturbed miombo woodland, of which 56.3 ± 11.8% is linked to the post‐harvest curing process. This deforestation represents 0.55 ± 0.06% of the wooded area of the Kipembawe Division. The perception of high incomes from tobacco cultivation has encouraged migration of both agriculturalists and pastoralists into the area, resulting in higher livestock numbers that lead to further degradation. Higher human populations need more woodland resources such as fuelwood and building materials and more farmland for food crops. Continued deforestation will reduce the long‐term profitability of tobacco cultivation due to a lack of fuel to cure the crop and could render production unviable. Action is urgently needed to conserve globally important biodiversity resources while enabling agricultural and pastoral activities to continue. Improved governance, together with sustainable land management strategies and diversification of livelihood strategies, can reduce dependence on tobacco cultivation and contribute to a sustainable future for this ecoregion

ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data

ForestQC: Quality control on genetic variants from next-generation sequencing data using random forest.

Next-generation sequencing technology (NGS) enables the discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in NGS or variant callers. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present ForestQC, a statistical tool for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our software uses the information on sequencing quality, such as sequencing depth, genotyping quality, and GC contents, to predict whether a particular variant is likely to be false-positive. To evaluate ForestQC, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that ForestQC outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. ForestQC is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is a practical approach to perform quality control on genetic variants from sequencing data

Environmental Influences on Physical Activity among Rural Adults in Montana, United States: Views from Built Environment Audits, Resident Focus Groups, and Key Informant Interviews

Rural populations in the United States have lower physical activity levels and are at a higher risk of being overweight and suffering from obesity than their urban counterparts. This paper aimed to understand the environmental factors that influence physical activity among rural adults in Montana. Eight built environment audits, 15 resident focus groups, and 24 key informant interviews were conducted between August and December 2014. Themes were triangulated and summarized into five categories of environmental factors: built, social, organizational, policy, and natural environments. Although the existence of active living features was documented by environmental audits, residents and key informants agreed that additional indoor recreation facilities and more well-maintained and conveniently located options were needed. Residents and key informants also agreed on the importance of age-specific, well-promoted, and structured physical activity programs, offered in socially supportive environments, as facilitators to physical activity. Key informants, however, noted that funding constraints and limited political will were barriers to developing these opportunities. Since building new recreational facilities and structures to support active transportation pose resource challenges, especially for rural communities, our results suggest that enhancing existing features, making small improvements, and involving stakeholders in the city planning process would be more fruitful to build momentum towards larger changes

Room Temperature Electrochemical Fluoride (De)Insertion into the Defect Pyrochlore CsMnFeF6

We report on the reversible, electrochemical (de)fluorination of the defect fluoride pyrochlore CsMnFeF6 at room temperature using a liquid electrolyte. CsMnFeF6 was synthesized via three different methods (hydrothermal, ceramic, and mechanochemical), each of which yield products of varying particle size and phase purity. Using galvanostatic cycling, we found that after three oxidative/ reductive cycles, approximately one fluoride ion can be reversibly inserted and removed from mechanochemically synthesized CsMnFeF6 for multiple cycles. Ex-situ X-ray absorption spectroscopy confirmed that both the Mn2+ and Fe3+ in this composition are redox active during cycling. Electrochemical impedance spectroscopy and ex-situ synchrotron powder diffraction were utilized to investigate the delayed onset of significant fluoride (de)insertion. We observed decreased impedance after one full cycle and subtle expansion and contraction of the CsMnFeF6 cubic lattice on oxidation (insertion) and reduction (removal), respectively, over the first two cycles. Our results suggest the formation of fluoride vacancies in early cycles generates mixed-valent Fe that enhances the conductivity and improves the reversibility in later cycles