S-D614G Mutation Reveals the Euro-America and East-Asia Origin SARS-CoV-2 Virus Spread in Indonesia

COVID-19 is a pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The first case was found in the city of Wuhan, Hubei province, China. The first case in Indonesia was reported in March 2020 and currently there are 0.5 million cases with a death rate of 3.1%. This rapid increase in cases is thought to due to presence of the mutant strain S-D614G, which causes a faster rate of infection and spread. The purpose of this study was to determine the presence of S-D614G mutations in Indonesian samples in order to find the origin of COVID-19 which was spread in Indonesia based on the Spike gene sequences and the RdRp genes from 25 countries, and one control sequence China/Wuhan-Hu-1 obtained from the NCBI and GISAID databases. Mutation analysis was carried out through multiple alignments using BioEdit software. Phylogenetic tree reconstruction using MEGA6 software with the Neighbor Joining method. This study found mutation of S-D614G in one Indonesian sample, namely the Indonesian/SBY9 sample along with 23 samples from Europe, America, and Africa. The phylogenetic tree reconstruction confirmed these findings; the mutated samples were closely related to samples from these continents, while the non-mutated Indonesian samples were closely related to sample from East Asia. These findings indicate that the origin of the SARS-CoV-2 virus in Indonesia possibly came from the East Asia cluster and the European-American cluster.

Genetic Diversity and Phylogenetic of Longtail Tuna (Thunnus tonggol) Landed in Pabean Fish Market, Surabaya

Indonesia is the biggest tuna exporter in Southeast Asia. With a high number of tuna catch, it is worried that the catch will decrease tuna population, specifically longtail tuna. To anticipate the decrease, there needs to be a conservation program to protect longtail tunas from scarcity. One method used to protect longtail tuna is by genetic conservation. The aim of this research is to understand the genetic and phylogenetic variety of the longtail tuna in Pabean Surabaya Fish Market. The polymerase chain reaction was used to amplify segment of the mitochondrial control region gene from members of these sample, used forward primer CRK 5’-AGCTC AGCGC CAGAG CGCCG GTCTT GTAAA-3’ and reverse primer CRE 5’-CCTGA AGTAG GAACC AGATG-3’. Based on the sequencing process, 28 out of 29 samples longtail tuna were analyzed successfully. The results of the 28 sample analysis of longtail tuna based on its genetic variety and phylogenetic tree reconstruction showed a haplotype variety (Hd) score of 1,00000, and nucleotide (π) variety of 0,1939. Genetic variety value showed that longtail tuna has great adaption capabilities toward environmental changes time to time. Phylogenetic tree reconstruction results showed 7 clades with a genetic range of 0,005 – 0,035, which shows that all samples are closely related. The results of this study can be used as basic information in forming regulations on longtail tuna sustainable management and genetic conservation.

Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks

Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.

Influence of alignment uncertainty on homology and phylogenetic modeling

Most evolutionary analyses or structure modeling are based upon pre-estimated multiple sequence alignment (MSA) models. From a computational point of view, it is too complex to estimate a correct alignment. Hence, increasing or identifying signal inside sequence alignment has intensified over the last few years. During the presentation, I would like to share two approaches, homology extension and sampling, on this topic. The first part, transmembrane proteins (TMPs) constitute about 20~30% of all protein coding genes. The relative lack of experimental structure has so far made it hard to develop specific alignment methods and the current state of the art (PRALINE™) only manages to recapitulate 50% of the positions in the reference alignments available from the BAliBASE2-ref7. We show how homology extension can be adapted and combined with a consistency based approach in order to significantly improve the multiple sequence alignment of alpha-helical TMPs. TM-Coffee is a special mode of PSI-Coffee able to efficiently align TMPs, while using a reduced reference database for homology extension. Our benchmarking on BAliBASE2-ref7 alpha-helical TMPs shows a significant improvement over the most accurate methods such as MSAProbs, Kalign, PROMALS, MAFFT, ProbCons and PRALINE™. The second part, homology and evolutionary modeling are the most common applications of MSAs. In this work, we show how this problem can be partly overcome using the transitive consistency score (TCS), an extended version of the T-Coffee scoring scheme. Using this local evaluation function, we show that one can identify the most reliable portions of an MSA, as judged from BAliBASE and PREFAB structure-based reference alignments. We also show how this measure can be used to improve phylogenetic tree reconstruction using both an established simulated data set and a novel empirical yeast data set. Our approach relies on the T-Coffee framework; it uses libraries of pairwise alignments to evaluate any third party MSA. We compared TCS with Heads-or-Tails, GUIDANCE, Gblocks, and trimAl and found it to lead to significantly better estimates of structural accuracy and more accurate phylogenetic trees. References: PSI/TM-Coffee: a web server for fast and accurate multiple sequence alignments of regular and transmembrane proteins using homology extension on reduced databases. Nucleic acids research 44, W339–343(2016). TCS: a web server for multiple sequence alignment evaluation and phylogenetic reconstruction. Nucleic acids research 43, W3–6 (2015). TCS: a new multiple sequence alignment reliability measure to estimate alignment accuracy and improve phylogenetic tree reconstruction.Molecular biology and evolution 31, 1625–37 (2014). Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee. Bmc Bioinformatics 13, S1 (2012). Website: PSI/TM-Coffee http://tcoffee.crg.cat/tmcoffee, TCS http://tcoffee.crg.cat/tcs

Influence of alignment uncertainty on homology and phylogenetic modeling

Most evolutionary analyses or structure modeling are based upon pre-estimated multiple sequence alignment (MSA) models. From a computational point of view, it is too complex to estimate a correct alignment. Hence, increasing or identifying signal inside sequence alignment has intensified over the last few years. During the presentation, I would like to share two approaches, homology extension and sampling, on this topic. The first part, transmembrane proteins (TMPs) constitute about 20~30% of all protein coding genes. The relative lack of experimental structure has so far made it hard to develop specific alignment methods and the current state of the art (PRALINE™) only manages to recapitulate 50% of the positions in the reference alignments available from the BAliBASE2-ref7. We show how homology extension can be adapted and combined with a consistency based approach in order to significantly improve the multiple sequence alignment of alpha-helical TMPs. TM-Coffee is a special mode of PSI-Coffee able to efficiently align TMPs, while using a reduced reference database for homology extension. Our benchmarking on BAliBASE2-ref7 alpha-helical TMPs shows a significant improvement over the most accurate methods such as MSAProbs, Kalign, PROMALS, MAFFT, ProbCons and PRALINE™. The second part, homology and evolutionary modeling are the most common applications of MSAs. In this work, we show how this problem can be partly overcome using the transitive consistency score (TCS), an extended version of the T-Coffee scoring scheme. Using this local evaluation function, we show that one can identify the most reliable portions of an MSA, as judged from BAliBASE and PREFAB structure-based reference alignments. We also show how this measure can be used to improve phylogenetic tree reconstruction using both an established simulated data set and a novel empirical yeast data set. Our approach relies on the T-Coffee framework; it uses libraries of pairwise alignments to evaluate any third party MSA. We compared TCS with Heads-or-Tails, GUIDANCE, Gblocks, and trimAl and found it to lead to significantly better estimates of structural accuracy and more accurate phylogenetic trees. References: PSI/TM-Coffee: a web server for fast and accurate multiple sequence alignments of regular and transmembrane proteins using homology extension on reduced databases. Nucleic acids research 44, W339–343(2016). TCS: a web server for multiple sequence alignment evaluation and phylogenetic reconstruction. Nucleic acids research 43, W3–6 (2015). TCS: a new multiple sequence alignment reliability measure to estimate alignment accuracy and improve phylogenetic tree reconstruction.Molecular biology and evolution 31, 1625–37 (2014). Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee. Bmc Bioinformatics 13, S1 (2012). Website: PSI/TM-Coffee http://tcoffee.crg.cat/tmcoffee, TCS http://tcoffee.crg.cat/tcs