MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale

In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).

Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach

Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household's power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.

HEATSTER: A Database and Web Server for Identification and Classification of Heat Stress Transcription Factors in Plants

Heat stress transcription factors (HSFs) regulate transcriptional response to a large number of environmental influences, such as temperature fluctuations and chemical compound applications. Plant HSFs represent a large and diverse gene family. The HSF members vary substantially both in gene expression patterns and molecular functions. HEATSTER is a web resource for mining, annotating, and analyzing members of the different classes of HSFs in plants. A web-interface allows the identification and class assignment of HSFs, intuitive searches in the database and visualization of conserved motifs, and domains to classify novel HSFs.

Genome Analysis of Conserved Dehydrin Motifs in Vascular Plants

Dehydrins, a large family of abiotic stress proteins, are defined by the presence of a mostly conserved motif known as the K-segment, and may also contain two other conserved motifs known as the Y-segment and S-segment. Using the dehydrin literature, we developed a sequence motif definition of the K-segment, which we used to create a large dataset of dehydrin sequences by searching the Pfam00257 dehydrin dataset and the Phytozome 10 sequences of vascular plants. A comprehensive analysis of these sequences reveals that lysine residues are highly conserved in the K-segment, while the amino acid type is often conserved at other positions. Despite the Y-segment name, the central tyrosine is somewhat conserved, but can be substituted with two other small aromatic amino acids (phenylalanine or histidine). The S-segment contains a series of serine residues, but in some proteins is also preceded by a conserved LHR sequence. In many dehydrins containing all three of these motifs the S-segment is linked to the K-segment by a GXGGRRKK motif (where X can be any amino acid), suggesting a functional linkage between these two motifs. An analysis of the sequences shows that the dehydrin architecture and several biochemical properties (isoelectric point, molecular mass, and hydrophobicity score) are dependent on each other, and that some dehydrin architectures are overexpressed during certain abiotic stress, suggesting that they may be optimized for a specific abiotic stress while others are involved in all forms of dehydration stress (drought, cold, and salinity).

ET-Motif: Solving the Exact (l, d)-Planted Motif Problem Using Error Tree Structure

Motif finding is an important and a challenging problem in many biological applications such as discovering promoters, enhancers, locus control regions, transcription factors, and more. The (l, d)-planted motif search, PMS, is one of several variations of the problem. In this problem, there are n given sequences over alphabets of size [Formula: see text], each of length m, and two given integers l and d. The problem is to find a motif m of length l, where in each sequence there is at least an l-mer at a Hamming distance of [Formula: see text] of m. In this article, we propose ET-Motif, an algorithm that can solve the PMS problem in [Formula: see text] time and [Formula: see text] space. The time bound can be further reduced by a factor of m with [Formula: see text] space. In case the suffix tree that is built for the input sequences is balanced, the problem can be solved in [Formula: see text] time and [Formula: see text] space. Similarly, the time bound can be reduced by a factor of m using [Formula: see text] space. Moreover, the variations of the problem, namely the edit distance PMS and edited PMS (Quorum), can be solved using ET-Motif with simple modifications but upper bands of space and time. For edit distance PMS, the time and space bounds will be increased by [Formula: see text], while for edited PMS the increase will be of [Formula: see text] in the time bound.

PMS6: a fast algorithm for motif discovery

We propose a new algorithm, PMS6, for the (l,d)-motif discovery problem in which we are to find all strings of length l that appear in every string of a given set of strings with at most d mismatches. The run time ratio PMS5/PMS6, where PMS5 is the fastest previously known algorithm for motif discovery in large instances, ranges from a high of 2.20 for the (21,8) challenge instances to a low of 1.69 for the (17,6) challenge instances. Both PMS5 and PMS6 require some amount of pre-processing. The pre-processing time for PMS6 is 34 times faster than that for PMS5 for (23,9) instances. When pre-processing time is factored in, the run time ratio PMS5/PMS6 is as high as 2.75 for (13,4) instances and as low as 1.95 for (17,6) instances.