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Profiling of 35 Cases of Hb S/Hb E (: c.20A>T/: c.79G>a), Disease and Association with α-Thalassemia and β-Globin Gene Cluster Haplotypes from Odisha, India.

Hemoglobin
Hb S/Hb E (: c.20A>T/: c.79G>A) is an uncommon variant of sickle cell disease resulting from coinheritance of Hb S and Hb E. Clinico-hematological and biochemical parameters of 35 cases of Hb S/Hb E disease were studied and compared with 70 matched c...

Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression ...

Microbial chassis engineering drives heterologous production of complex secondary metabolites.

Biotechnology advances
The cryptic secondary metabolite biosynthetic gene clusters (BGCs) far outnumber currently known secondary metabolites. Heterologous production of secondary metabolite BGCs in suitable chassis facilitates yield improvement and discovery of new-to-nat...

Deep Learning to Predict the Biosynthetic Gene Clusters in Bacterial Genomes.

Journal of molecular biology
Biosynthetic gene clusters (BGCs) in bacterial genomes code for important small molecules and secondary metabolites. Based on the validated BGCs and the corresponding sequences of protein family domains (Pfams), Pfam functions and clan information, w...

CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.

Interdisciplinary sciences, computational life sciences
The sluggish pace of new antibacterial drug development reflects a vulnerability in the face of the current severe threat posed by bacterial resistance. Microbial natural products (NPs), as a reservoir of immense chemical potential, have emerged as t...

Machine-Learning Analysis of Streptomyces coelicolor Transcriptomes Reveals a Transcription Regulatory Network Encompassing Biosynthetic Gene Clusters.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Streptomyces produces diverse secondary metabolites of biopharmaceutical importance, yet the rate of biosynthesis of these metabolites is often hampered by complex transcriptional regulation. Therefore, a fundamental understanding of transcriptional ...

Machine learning uncovers the transcriptional regulatory network for the production host Streptomyces albidoflavus.

Cell reports
Streptomyces albidoflavus is a widely used strain for natural product discovery and production through heterologous biosynthetic gene clusters (BGCs). However, the transcriptional regulatory network (TRN) and its impact on secondary metabolism remain...

DeepES: deep learning-based enzyme screening to identify orphan enzyme genes.

Bioinformatics (Oxford, England)
MOTIVATION: Progress in sequencing technology has led to determination of large numbers of protein sequences, and large enzyme databases are now available. Although many computational tools for enzyme annotation were developed, sequence information i...

Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.

Nucleic acids research
Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To unearth this vast potential, we present BGC-Prophet, a transformer-based language model for BGC predict...

Discovery of naturally inspired antimicrobial peptides using deep learning.

Bioorganic chemistry
Non-ribosomal peptides (NRPs) are promising lead compounds for novel antibiotics. Bioinformatic mining of silent microbial NRPS gene clusters provide crucial insights for the discovery and de novo design of bioactive peptides. Here, we describe the e...