A 2b-RAD parentage analysis pipeline for complex and mixed DNA samples

AbstractAlthough incompletely understood, microbiota-host interactions are assumed to be altered in irritable bowel syndrome (IBS). We, therefore, aimed to develop a novel analysis pipeline tailored for the integrative analysis of microbiota-host interactions and association to symptoms and prove its utility in a pilot cohort. A multilayer stepwise integrative analysis pipeline was developed to visualize complex variable associations. Application of the pipeline was demonstrated on a dataset of IBS patients and healthy controls (HC), using the R software package to analyze colonic host mRNA and mucosal microbiota (16S rRNA gene sequencing), as well as gastrointestinal (GI) and psychological symptoms. In total, 42 IBS patients (57% female, mean age 33.6 (range 18–58)) and 20 HC (60% female, mean age 26.8 (range 23–41)) were included. Only in IBS patients, mRNA expression of Toll-like receptor 4 and genes associated with barrier function (PAR2, OCLN, TJP1) intercorrelated closely, suggesting potential functional relationships. This host genes-based “permeability cluster” was associated to mucosa-adjacent Chlamydiae and Lentisphaerae, and furthermore associated to satiety as well as to anxiety, depression and fatigue. In both IBS patients and HC, chromogranins, secretogranins and TLRs clustered together. In IBS patients, this host genes-based “immune-enteroendocrine cluster” was associated to specific members of Firmicutes, and to depression and fatigue, whereas in HC no significant association to microbiota was identified. We have developed a stepwise integrative analysis pipeline that allowed identification of unique host-microbiota intercorrelation patterns and association to symptoms in IBS patients. This analysis pipeline may aid in advancing the understanding of complex variable associations in health and disease.

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Making waves in Breedbase: An integrated spectral data storage and analysis pipeline for plant breeding programs

tppj ◽

10.1002/ppj2.20012 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Jenna Hershberger ◽

Nicolas Morales ◽

Christiano C. Simoes ◽

Bryan Ellerbrock ◽

Guillaume Bauchet ◽

...

Keyword(s):

Plant Breeding ◽

Spectral Data ◽

Data Storage ◽

Analysis Pipeline ◽

Breeding Programs

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Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field

Plants ◽

10.3390/plants10050840 ◽

2021 ◽

Vol 10 (5) ◽

pp. 840

Author(s):

Willem Q. M. van de Koot ◽

Larissa J. J. van Vliet ◽

Weilun Chen ◽

John H. Doonan ◽

Candida Nibau

Keyword(s):

Image Analysis ◽

Organic Matter ◽

Plant Density ◽

Important Determinant ◽

Carbon Sink ◽

High Accuracy ◽

Analysis Pipeline ◽

Colony Density ◽

Water Table Management ◽

And Performance

Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland’s function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.

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RAEMAP: Real-Time Advanced Eye Movements Analysis Pipeline

Symposium on Eye Tracking Research and Applications ◽

10.1145/3379157.3391992 ◽

2020 ◽

Author(s):

Gavindya Jayawardena

Keyword(s):

Eye Movements ◽

Real Time ◽

Analysis Pipeline

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Machine learning-based classification of mitochondrial morphology in primary neurons and brain

Scientific Reports ◽

10.1038/s41598-021-84528-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Garrett M. Fogo ◽

Anthony R. Anzell ◽

Kathleen J. Maheras ◽

Sarita Raghunayakula ◽

Joseph M. Wider ◽

...

Keyword(s):

Image Analysis ◽

Cortical Neurons ◽

Mitochondrial Dynamics ◽

Automated Image Analysis ◽

Mitochondrial Morphology ◽

Analysis Pipeline ◽

Genetic Ablation ◽

Block Face

AbstractThe mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeostatic mitochondrial profile can change morphologic distribution in response to various stressors. Therefore, it is imperative to develop a method that robustly measures mitochondrial morphology with high accuracy. Here, we developed a semi-automated image analysis pipeline for the quantitation of mitochondrial morphology for both in vitro and in vivo applications. The image analysis pipeline was generated and validated utilizing images of primary cortical neurons from transgenic mice, allowing genetic ablation of key components of mitochondrial dynamics. This analysis pipeline was further extended to evaluate mitochondrial morphology in vivo through immunolabeling of brain sections as well as serial block-face scanning electron microscopy. These data demonstrate a highly specific and sensitive method that accurately classifies distinct physiological and pathological mitochondrial morphologies. Furthermore, this workflow employs the use of readily available, free open-source software designed for high throughput image processing, segmentation, and analysis that is customizable to various biological models.

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Monitoring the microbiome for food safety and quality using deep shotgun sequencing

npj Science of Food ◽

10.1038/s41538-020-00083-y ◽

2021 ◽

Vol 5 (1) ◽

Cited By ~ 1

Author(s):

Kristen L. Beck ◽

Niina Haiminen ◽

David Chambliss ◽

Stefan Edlund ◽

Mark Kunitomi ◽

...

Keyword(s):

Rna Sequencing ◽

Environmental Changes ◽

Pet Food ◽

Analysis Pipeline ◽

Multiple Sequence ◽

Food Ingredients ◽

Total Rna ◽

Specific Species ◽

Ingredient Composition ◽

Food Safety And Quality

AbstractIn this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species’ viability from total RNA sequencing.

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Parentage Analysis in Giant Grouper (Epinephelus lanceolatus) Using Microsatellite and SNP Markers from Genotyping-by-Sequencing Data

Genes ◽

10.3390/genes12071042 ◽

2021 ◽

Vol 12 (7) ◽

pp. 1042

Author(s):

Zhuoying Weng ◽

Yang Yang ◽

Xi Wang ◽

Lina Wu ◽

Sijie Hua ◽

...

Keyword(s):

Fishery Management ◽

Genotyping By Sequencing ◽

Parentage Analysis ◽

Snp Markers ◽

Individual Identification ◽

Pedigree Information ◽

Nucleotide Polymorphisms ◽

Sequencing Data ◽

Polymorphic Snps ◽

Mixed Family

Pedigree information is necessary for the maintenance of diversity for wild and captive populations. Accurate pedigree is determined by molecular marker-based parentage analysis, which may be influenced by the polymorphism and number of markers, integrity of samples, relatedness of parents, or different analysis programs. Here, we described the first development of 208 single nucleotide polymorphisms (SNPs) and 11 microsatellites for giant grouper (Epinephelus lanceolatus) taking advantage of Genotyping-by-sequencing (GBS), and compared the power of SNPs and microsatellites for parentage and relatedness analysis, based on a mixed family composed of 4 candidate females, 4 candidate males and 289 offspring. CERVUS, PAPA and COLONY were used for mutually verification. We found that SNPs had a better potential for relatedness estimation, exclusion of non-parentage and individual identification than microsatellites, and > 98% accuracy of parentage assignment could be achieved by 100 polymorphic SNPs (MAF cut-off < 0.4) or 10 polymorphic microsatellites (mean Ho = 0.821, mean PIC = 0.651). This study provides a reference for the development of molecular markers for parentage analysis taking advantage of next-generation sequencing, and contributes to the molecular breeding, fishery management and population conservation.

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xCELLanalyzer: A Framework for the Analysis of Cellular Impedance Measurements for Mode of Action Discovery

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555218819459 ◽

2019 ◽

Vol 24 (3) ◽

pp. 213-223 ◽

Cited By ~ 1

Author(s):

Raimo Franke ◽

Bettina Hinkelmann ◽

Verena Fetz ◽

Theresia Stradal ◽

Florenz Sasse ◽

...

Keyword(s):

Data Analysis ◽

Bioactive Compounds ◽

Mode Of Action ◽

Mammalian Cells ◽

Cellular Response ◽

Label Free ◽

Synthesis Inhibitor ◽

Analysis Pipeline ◽

Bioactive Natural Products ◽

Data Analysis Pipeline

Mode of action (MoA) identification of bioactive compounds is very often a challenging and time-consuming task. We used a label-free kinetic profiling method based on an impedance readout to monitor the time-dependent cellular response profiles for the interaction of bioactive natural products and other small molecules with mammalian cells. Such approaches have been rarely used so far due to the lack of data mining tools to properly capture the characteristics of the impedance curves. We developed a data analysis pipeline for the xCELLigence Real-Time Cell Analysis detection platform to process the data, assess and score their reproducibility, and provide rank-based MoA predictions for a reference set of 60 bioactive compounds. The method can reveal additional, previously unknown targets, as exemplified by the identification of tubulin-destabilizing activities of the RNA synthesis inhibitor actinomycin D and the effects on DNA replication of vioprolide A. The data analysis pipeline is based on the statistical programming language R and is available to the scientific community through a GitHub repository.

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