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1. UPP2: fast and accurate alignment of datasets with fragmentary sequences

   https://doi.org/10.1093/bioinformatics/btad007  

   Park, Minhyuk; Ivanovic, Stefan; Chu, Gillian; Shen, Chengze; Warnow, Tandy; Marschall, ed., Tobias (January 2023, Bioinformatics)

   Abstract MotivationMultiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. Ultra-large multiple sequence alignment using Phylogeny-aware Profiles (UPP) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full-length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets. ResultsWe present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show that UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity and is among the most accurate otherwise. Availability and implementationhttps://github.com/gillichu/sepp. Supplementary informationSupplementary data are available at Bioinformatics online. 

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2. NEFFy: A Versatile Tool for Computing the Number of Effective Sequences

   https://doi.org/10.1093/bioinformatics/btaf222  

   Haghani, Maryam; Bhattacharya, Debswapna; Murali, T M (June 2025, Bioinformatics)

   Cheng, Jianlin (Ed.)

   Abstract MotivationA Multiple Sequence Alignment (MSA) contains fundamental evolutionary information that is useful in the prediction of structure and function of proteins and nucleic acids. The “Number of Effective Sequences” (NEFF) quantifies the diversity of sequences of an MSA. While several tools embed NEFF calculation with various options, none are standalone tools for this purpose, and they do not offer all the available options. ResultsWe developed NEFFy, the first software package to integrate all these options and calculate NEFF across diverse MSA formats for proteins, RNAs, and DNAs. It surpasses existing tools in functionality without compromising computational efficiency and scalability. NEFFy also offers per-residue NEFF calculation and supports NEFF computation for MSAs of multimeric proteins, with the capability to be extended to DNAs and RNAs. Availability and ImplementationNEFFy is released as open-source software under the GNU Public License v3.0. The source code in C ++ and a Python wrapper are available at https://github.com/Maryam-Haghani/NEFFy. To ensure users can fully leverage these capabilities, comprehensive documentation and examples are provided at https://Maryam-Haghani.github.io/NEFFy. Supplementary InformationSupplementary data are available at Bioinformatics online. 

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3. DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins

   https://doi.org/10.1093/bioinformatics/btz863  

   Zhang, Chengxin; Zheng, Wei; Mortuza, S M; Li, Yang; Zhang, Yang; Valencia, Alfonso (November 2019, Bioinformatics)

   Abstract Motivation The success of genome sequencing techniques has resulted in rapid explosion of protein sequences. Collections of multiple homologous sequences can provide critical information to the modeling of structure and function of unknown proteins. There are however no standard and efficient pipeline available for sensitive multiple sequence alignment (MSA) collection. This is particularly challenging when large whole-genome and metagenome databases are involved. Results We developed DeepMSA, a new open-source method for sensitive MSA construction, which has homologous sequences and alignments created from multi-sources of whole-genome and metagenome databases through complementary hidden Markov model algorithms. The practical usefulness of the pipeline was examined in three large-scale benchmark experiments based on 614 non-redundant proteins. First, DeepMSA was utilized to generate MSAs for residue-level contact prediction by six coevolution and deep learning-based programs, which resulted in an accuracy increase in long-range contacts by up to 24.4% compared to the default programs. Next, multiple threading programs are performed for homologous structure identification, where the average TM-score of the template alignments has over 7.5% increases with the use of the new DeepMSA profiles. Finally, DeepMSA was used for secondary structure prediction and resulted in statistically significant improvements in the Q3 accuracy. It is noted that all these improvements were achieved without re-training the parameters and neural-network models, demonstrating the robustness and general usefulness of the DeepMSA in protein structural bioinformatics applications, especially for targets without homologous templates in the PDB library. Availability and implementation https://zhanglab.ccmb.med.umich.edu/DeepMSA/. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. eCOMPASS: evaluative comparison of multiple protein alignments by statistical score

   https://doi.org/10.1093/bioinformatics/btab374  

   Neuwald, Andrew F; Kolaczkowski, Bryan D; Altschul, Stephen F (May 2021, Bioinformatics)

   Ponty, Yann (Ed.)

   Abstract Motivation Detecting subtle biologically relevant patterns in protein sequences often requires the construction of a large and accurate multiple sequence alignment (MSA). Methods for constructing MSAs are usually evaluated using benchmark alignments, which, however, typically contain very few sequences and are therefore inappropriate when dealing with large numbers of proteins. Results eCOMPASS addresses this problem using a statistical measure of relative alignment quality based on direct coupling analysis (DCA): To maintain protein structural integrity over evolutionary time, substitutions at one residue position typically result in compensating substitutions at other positions. eCOMPASS computes the statistical significance of the congruence between high scoring directly coupled pairs and 3D contacts in corresponding structures, which depends upon properly aligned homologous residues. We illustrate eCOMPASS using both simulated and real MSAs. Availability and Implementation The eCOMPASS executable, C ++ open source code and input data sets are available at https://www.igs.umaryland.edu/labs/neuwald/software/compass. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. MAGUS+eHMMs: improved multiple sequence alignment accuracy for fragmentary sequences

   Shen, Chengze; Zaharias, Paul; Warnow, Tandy (January 2022, Bioinformatics)

   Boeva, Valentina (Ed.)

   Abstract Summary Multiple sequence alignment is an initial step in many bioinformatics pipelines, including phylogeny estimation, protein structure prediction and taxonomic identification of reads produced in amplicon or metagenomic datasets, etc. Yet, alignment estimation is challenging on datasets that exhibit substantial sequence length heterogeneity, and especially when the datasets have fragmentary sequences as a result of including reads or contigs generated by next-generation sequencing technologies. Here, we examine techniques that have been developed to improve alignment estimation when datasets contain substantial numbers of fragmentary sequences. We find that MAGUS, a recently developed MSA method, is fairly robust to fragmentary sequences under many conditions, and that using a two-stage approach where MAGUS is used to align selected ‘backbone sequences’ and the remaining sequences are added into the alignment using ensembles of Hidden Markov Models further improves alignment accuracy. The combination of MAGUS with the ensemble of eHMMs (i.e. MAGUS+eHMMs) clearly improves on UPP, the previous leading method for aligning datasets with high levels of fragmentation. Availability and implementation UPP is available on https://github.com/smirarab/sepp, and MAGUS is available on https://github.com/vlasmirnov/MAGUS. MAGUS+eHMMs can be performed by running MAGUS to obtain the backbone alignment, and then using the backbone alignment as an input to UPP. Supplementary information Supplementary data are available at Bioinformatics online. 

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