TheSNPpit—A High Performance Database System for Managing Large Scale SNP Data

Groeneveld, Eildert; Lichtenberg, Helmut

doi:10.1371/journal.pone.0164043

Cited by 9 publications

(10 citation statements)

References 11 publications

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“…) and the SNPpit (Excoffier & Heckel ; Marjoram & Tavaré ; Conesa et al . ; Groeneveld & Lichtenberg ). Some of the publicly available resources are summarized in Table .…”

Section: Preliminary Checks and Planningmentioning

confidence: 99%

Lessons learnt on the analysis of large sequence data in animal genomics

Biscarini

Cozzi²,

Wengel

2018

Animal Genetics

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The 'omics revolution has made a large amount of sequence data available to researchers and the industry. This has had a profound impact in the field of bioinformatics, stimulating unprecedented advancements in this discipline. Mostly, this is usually looked at from the perspective of human 'omics, in particular human genomics. Plant and animal genomics, however, have also been deeply influenced by next-generation sequencing technologies, with several genomics applications now popular among researchers and the breeding industry. Genomics tends to generate huge amounts of data, and genomic sequence data account for an increasing proportion of big data in biological sciences, due largely to decreasing sequencing and genotyping costs and to large-scale sequencing and resequencing projects. The analysis of big data poses a challenge to scientists, as data gathering currently takes place at a faster pace than does data processing and analysis, and the associated computational burden is increasingly taxing, making even simple manipulation, visualization and transferring of data a cumbersome operation. The time consumed by the processing and analysing of huge data sets may be at the expense of data quality assessment and critical interpretation. Additionally, when analysing lots of data, something is likely to go awry-the software may crash or stop-and it can be very frustrating to track the error. We herein review the most relevant issues related to tackling these challenges and problems, from the perspective of animal genomics, and provide researchers that lack extensive computing experience with guidelines that will help when processing large genomic data sets.

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“…) and the SNPpit (Excoffier & Heckel ; Marjoram & Tavaré ; Conesa et al . ; Groeneveld & Lichtenberg ). Some of the publicly available resources are summarized in Table .…”

Section: Preliminary Checks and Planningmentioning

confidence: 99%

Lessons learnt on the analysis of large sequence data in animal genomics

Biscarini

Cozzi²,

Wengel

2018

Animal Genetics

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“…So far, the most compelling tool is the recently developed open source system TheSNPpit (Groeneveld and Lichtenberg, 2016). It allows for an integration of large genetic datasets in a PostgreSQL environment, which is also the backend of most GIS databases.…”

Section: Perspectivementioning

confidence: 99%

Simple Rules for an Efficient Use of Geographic Information Systems in Molecular Ecology

et al. 2017

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Geographic Information Systems (GIS) are becoming increasingly popular in the context of molecular ecology and conservation biology thanks to their display options efficiency, flexibility and management of geodata. Indeed, spatial data for wildlife and livestock species is becoming a trend with many researchers publishing genomic data that is specifically suitable for landscape studies. GIS uniquely reveal the possibility to overlay genetic information with environmental data and, as such, allow us to locate and analyze genetic boundaries of various plant and animal species or to study gene-environment associations (GEA). This means that, using GIS, we can potentially identify the genetic bases of species adaptation to particular geographic conditions or to climate change. However, many biologists are not familiar with the use of GIS and underlying concepts and thus experience difficulties in finding relevant information and instructions on how to use them. In this paper, we illustrate the power of free and open source GIS approaches and provide essential information for their successful application in molecular ecology. First, we introduce key concepts related to GIS that are too often overlooked in the literature, for example coordinate systems, GPS accuracy and scale. We then provide an overview of the most employed open-source GIS-related software, file formats and refer to major environmental databases. We also reconsider sampling strategies as high costs of Next Generation Sequencing (NGS) data currently diminish the number of samples that can be sequenced per location. Thereafter, we detail methods of data exploration and spatial statistics suited for the analysis of large genetic datasets. Finally, we provide suggestions to properly edit maps and to make them as comprehensive as possible, either manually or trough programming languages.

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“…TheSNPpit (Groeneveld and Lichtenberg, 2016). It allows for an integration of large genetic datasets in a PostgreSQL environment, which is also the backend of most GIS databases.…”

Section: Perspectivementioning

confidence: 99%

Simple rules for an efficient use of Geographic Information Systems in molecular ecology

Leempoel

Duruz

Rochat

et al. 2017

Preprint

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Geographic Information Systems (GIS) are becoming increasingly popular in the context of molecular ecology and conservation biology thanks to their display options efficiency, flexibility and management of geodata. Indeed, spatial data for wildlife and livestock species is becoming a trend with many researchers publishing genomic data that is specifically suitable for landscape studies. GIS uniquely reveal the possibility to overlay genetic information with environmental data and, as such, allow us to locate and analyze genetic boundaries of various plant and animal species or to study gene-environment associations (GEA). This means that, using GIS, we can potentially identify the genetic bases of species adaptation to particular geographic conditions or to climate change. However, many biologists are not familiar with the use of GIS and underlying concepts and thus experience difficulties in finding relevant information and instructions on how to use them. In this paper, we illustrate the power of free and open source GIS approaches and provide essential information for their successful application in molecular ecology. First, we introduce key concepts related to GIS than are too often overlooked in the literature, for example coordinate systems, GPS accuracy and scale. We then provide an overview of the most employed open-source GIS-related software, file formats and refer to major environmental databases.We also reconsider sampling strategies as high costs of Next Generation Sequencing (NGS) data currently diminish the number of samples that can be sequenced per location. Thereafter, we detail methods of data exploration and spatial statistics suited for the analysis of large genetic datasets. Finally, we provide suggestions to properly edit maps and to make them as comprehensive as possible, either manually or trough programming languages.

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TheSNPpit—A High Performance Database System for Managing Large Scale SNP Data

Cited by 9 publications

References 11 publications

Lessons learnt on the analysis of large sequence data in animal genomics

Lessons learnt on the analysis of large sequence data in animal genomics

Simple Rules for an Efficient Use of Geographic Information Systems in Molecular Ecology

Simple rules for an efficient use of Geographic Information Systems in molecular ecology

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