GIS-Based Tool for Pest Specific Area-Wide Planning of Crop Rotation Distance with Land Use Data

Crop rotation is an important strategy for pest reduction. For mono-, or oligophagous pests that overwinter at a previously infested site, crop rotation means that the pests must find new host crop sites in the following year, and it is more efficient if a pest-specific distance is applied. Here, we report the development of a GIS-based tool for efficient cultivation planning using the example of the pest complex pea moth (Cydia nigricana) and grain and green peas (Pisum sativum). Monitoring data for four consecutive years (2016-2019) from 513 sites were used. Infestation of pea seeds and the distance to the previous year's pea sites were recorded. An adjustable Python script was developed by means of infestation-distance-correlation as a pest and crop-specific minimum migration distance (MD). The output of the tool is a risk map as decision support for cultivation planning. It shows different risk buffers with distances from 1261 m to 1825 m, depending on the cultivation type. The web tool is easily adjustable to other pests and crops anywhere in the world. The tool helps to prevent damages caused by agricultural, mono-, or oligophagous insect pests and consequently reduces pesticide applications for the benefit of the environment and biodiversity.

A Python script to design primers for overlap extension PCR to ligate two DNA fragments

Ligating two or more DNA fragments is a regular operation for the subcloning and the engineering of vectors. The overlap extension PCR serves as a straightforward method to solve this issue. However, it takes a relatively long time to design the appropriate overlapping primers and the primers for the full-length sequence, and there has not been a professional offline software for such kind of primer design. Here, we propose a Python script to search, calculate and sort thousands of combinations of primers for users according to the predefined parameters. The results of script running and experimental validation show that this script is capable of generating the optimal pairs of primers based on the proper melting temperatures and lengths of the primers, which facilitates gene modification in research.

Genetic Characterization of Brucella spp.: Whole Genome Sequencing-Based Approach for the Determination of Multiple Locus Variable Number Tandem Repeat Profiles

Brucellosis is an important zoonosis that is emerging in some regions of the world, gaining increased relevance with the inclusion of the causing agent Brucella spp. in the class B bioterrorism group. Until now, multi-locus VNTR Analysis (MLVA) based on 16 loci has been considered as the gold standard for Brucella typing. However, this methodology is laborious, and, with the rampant release of Brucella genomes, the transition from the traditional MLVA to whole genome sequencing (WGS)-based typing is on course. Nevertheless, in order to avoid a disruptive transition with the loss of massive genetic data obtained throughout the last decade and considering that the transition timings will vary considerably among different countries, it is important to determine WGS-based MLVA alleles of the nowadays sequenced genomes. On this regard, we aimed to evaluate the performance of a Python script that had been previously developed for the rapid in silico extraction of the MLVA alleles, by comparing it to the PCR-based MLVA procedure over 83 strains from different Brucella species. The WGS-based MLVA approach detected 95.3% of all possible 1,328 hits (83 strains×16 loci) and showed an agreement rate with the PCR-based MLVA procedure of 96.4% for MLVA-16. According to our dataset, we suggest the use of a minimal depth of coverage of ~50x and a maximum number of ~200 contigs as guiding “boundaries” for the future application of the script. In conclusion, the evaluated script seems to be a very useful and robust tool for the in silico determination of MLVA profiles of Brucella strains, allowing retrospective and prospective molecular epidemiological studies, which are important for maintaining an active epidemiological surveillance of brucellosis.

A Python script to merge Sanger sequences

Merging Sanger sequences is frequently needed during the gene cloning process. In this study, we provide a Python script that is able to assemble multiple overlapping Sanger sequences. The script utilizes the overlapping regions within the tandem Sanger sequences to merge the Sanger sequences. The results demonstrate that the script can produce the merged sequence from the input Sanger sequences in a single run. The script offers a simple and free method for merging Sanger sequences and is useful for gene cloning.

Snow cover area analysis and its relation with climate variability in Chandra basin, Western Himalaya, during 2001-2017 using MODIS and ERA5 data

Glaciers and snow cover area (SCA) plays an important role in river runoff in Himalayan region. There is a need to monitor SCA on spatio-temporal basis for better and efficient utilization of water resources. Moderate Resolution Imaging Spectroradiometer (MODIS) provides less cloudy data due to high temporal resolution as compared to other optical sensors for high elevation regions, and its 8-day snow cover product is globally used for snow cover estimation. The main objective of the present paper is to estimate annual and seasonal SCA in Chandra basin, Western Himalaya, and analysis of its variation with elevation, aspect, and slope during 2001 to 2017 using MODIS Terra (MOD10A2) and Aqua (MYD10A2) snow cover product as well as to correlate the same with temperature and precipitation using fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the global climate (ERA5) data. The total average SCA observed is 84.94% of basin area during the study period. The maximum annual average SCA was found as 91.23% in 2009 with minimum being 76.37% in 2016. Strong correlation is observed in annual and seasonal SCA with temperature which indicate that SCA variability is highly sensitive to temperature.