Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Proteomics

A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae

Numerous new technologies have been implemented in image analysis methods that help researchers draw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Herein lies the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine-learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. The results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures. As expected, machine-learning methods applied to digital image analysis can overcome the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their strong nutritional qualities and biological plasticity.

airpg: automatically accessing the inverted repeats of archived plastid genomes

BACKGROUND

In most flowering plants, the plastid genome exhibits a quadripartite genome structure, comprising a large and a small single copy as well as two inverted repeat regions. Thousands of plastid genomes have been sequenced and submitted to public sequence repositories in recent years. The quality of sequence annotations in many of these submissions is known to be problematic, especially regarding annotations that specify the length and location of the inverted repeats: such annotations are either missing or portray the length or location of the repeats incorrectly. However, many biological investigations employ publicly available plastid genomes at face value and implicitly assume the correctness of their sequence annotations.

RESULTS

We introduce airpg, a Python package that automatically assesses the frequency of incomplete or incorrect annotations of the inverted repeats among publicly available plastid genomes. Specifically, the tool automatically retrieves plastid genomes from NCBI Nucleotide under variable search parameters, surveys them for length and location specifications of inverted repeats, and confirms any inverted repeat annotations through self-comparisons of the genome sequences. The package also includes functionality for automatic identification and removal of duplicate genome records and accounts for taxa that genuinely lack inverted repeats. A survey of the presence of inverted repeat annotations among all plastid genomes of flowering plants submitted to NCBI Nucleotide until the end of 2020 using airpg, followed by a statistical analysis of potential associations with record metadata, highlights that release year and publication status of the genome records have a significant effect on the frequency of complete and equal-length inverted repeat annotations.

CONCLUSION

The number of plastid genomes on NCBI Nucleotide has increased dramatically in recent years, and many more genomes will likely be submitted over the next decade. airpg enables researchers to automatically access and evaluate the inverted repeats of these plastid genomes as well as their sequence annotations and, thus, contributes to increasing the reliability of publicly available plastid genomes. The software is freely available via the Python package index at http://pypi.python.org/pypi/airpg .

Bioinformatics in Germany: toward a national-level infrastructure

The German Network for Bioinformatics Infrastructure (de.NBI) is a national initiative funded by the German Federal Ministry of Education and Research (BMBF). The mission of de.NBI is (i) to provide high-quality bioinformatics services to users in basic and applied life sciences research from academia, industry and biomedicine; (ii) to offer bioinformatics training to users in Germany and Europe through a wide range of workshops and courses; and (iii) to foster the cooperation of the German bioinformatics community with international network structures such as the European life-sciences Infrastructure for biological Information (ELIXIR). The network was launched by the BMBF in March 2015 and now includes 40 service projects operated by 30 project partners that are organized in eight service centers. The de.NBI staff develops further and maintains almost 100 bioinformatics services for the human, plant and microbial research fields and provides comprehensive training courses to support users with different expertise levels in bioinformatics. In the future, de.NBI will expand its activities to the European level, as the de.NBI consortium was assigned by the BMBF to establish and run the German node of ELIXIR.

PhytoTypeDB: a database of plant protein inter-cultivar variability and function

Despite a fast-growing number of available plant genomes, available computational resources are poorly integrated and provide only limited access to the underlying data. Most existing databases focus on DNA/RNA data or specific gene families, with less emphasis on protein structure, function and variability. In particular, despite the economic importance of many plant accessions, there are no straightforward ways to retrieve or visualize information on their differences. To fill this gap, we developed PhytoTypeDB (http://phytotypedb.bio.unipd.it/), a scalable database containing plant protein annotations and genetic variants from resequencing of different accessions. The database content is generated by an integrated pipeline, exploiting state-of-the-art methods for protein characterization requiring only the proteome reference sequence and variant calling files. Protein names for unknown proteins are inferred by homology for over 95% of the entries. Single-nucleotide variants are visualized along with protein annotation in a user-friendly web interface. The server offers an effective querying system, which allows to compare variability among different species and accessions, to generate custom data sets based on shared functional features or to perform sequence searches. A documented set of exposed RESTful endpoints make the data accessible programmatically by third-party clients.