Biology subjects

Maranga, M.

Publications and source records attributed to Maranga, M..

3 recordsLinked to original sources

bioRxiv2023

Chromosome-scale assembly of the African yam bean genome

Genomics-informed breeding of locally adapted, nutritious, albeit underutilised African crops can help mitigate food and nutrition insecurity challenges in Africa, particularly against the backdrop of climate change. However, utilisation of modern crop improvement tools including genomic selection and genome editing for many African indigenous crops is hampered by the scarcity of genetic and genomic resources. Here we report on the assembly of the genome of African yam bean (Sphenostylis stenocarpa), a tuberous legume crop that is indigenous to Africa. By combining long and short read sequencing with Hi-C scaffolding, we produced a chromosome-scale assembly with an N50 of 69.5 Mbp and totalling 649 Mbp in length (77 - 81% of the estimated genome size based on flow cytometry). Using transcriptome evidence from Nanopore RNA-Seq and homology evidence from related crops, we annotated 31,614 putative protein coding genes. We further show how this resource improves anchoring of markers, genome-wide association analysis and candidate gene analyses in Africa yam bean. This genome assembly provides a valuable resource for genetic research in Africa yam bean.

genomics↗

bioRxiv2022

Comprehensive function annotation of metagenomes and microbial genomes using a deep learning-based method

Comprehensive protein function annotation is essential for understanding microbiome-related disease mechanisms in the host organisms. Still, a large portion of human gut microbial proteins lack functional annotation. Here, we have developed a new metagenome analysis workflow integrating de novo genome reconstruction, taxonomic profiling and deep learning-based functional annotations from DeepFRI. We validate DeepFRI functional annotations by comparing them to orthology-based annotations from eggNOG on a set of 1,070 infant metagenome samples from the DIABIMMUNE cohort. Using the workflow, we have generated a sequence catalogue of 1.9 million non-redundant microbial genes. The functional annotations revealed 70% concordance between GO annotations predicted by DeepFRI and eggNOG. However, DeepFRI improved the annotation coverage, with 99% of the gene catalogue obtaining GO molecular function annotations, albeit less specific compared to eggNOG. Additionally, we construct pan-genomes in a reference-free manner using high-quality metagenome assembled genomes (MAGs) and analyse the associated annotations. eggNOG annotated more genes on well-studied organisms such as Escherichia coli while DeepFRI was less sensitive to taxa. This workflow will contribute to novel understanding of the functional signature of the human gut microbiome in health and disease as well as guide future metagenomics studies.

bioinformatics↗

bioRxiv2022

Chromosome-scale assembly of the lablab genome - A model for inclusive orphan crop genomics

Orphan crops (also described as underutilised and neglected crops) hold the key to diversified and climate-resilient food systems. After decades of neglect, the genome sequencing of orphan crops is gathering pace, providing the foundations for their accelerated domestication and improvement. Recent attention has however turned to the gross under-representation of researchers in Africa in the genome sequencing efforts of their indigenous orphan crops. Here we report a radically inclusive approach to orphan crop genomics using the case of Lablab purpureus (L.) Sweet (syn. Dolichos lablab, or hyacinth bean) - a legume native to Africa and cultivated throughout the tropics for food and forage. Our Africa-led South-North plant genome collaboration produced a high-quality chromosomescale assembly of the lablab genome - the first chromosome-scale plant genome assembly locally sequenced in Africa. We also re-sequenced cultivated and wild accessions of lablab from Africa confirming two domestication events and examined the genetic diversity in lablab germplasm conserved in Africa. Our approach provides a valuable resource for lablab improvement and also presents a model that could be explored by other researchers sequencing indigenous crops particularly from Low and middle income countries (LMIC).

genomics↗