COVID-19 Evolves in Human Hosts

Y, Li; B, Liu; Wang, Z; Cui, Jian; Yao, Kangde; P, Lv; Shen, Yue; Xu, Yu; Guan, Yihan; Ma, Xin

doi:10.2139/ssrn.3562070

Cited by 6 publications

(10 citation statements)

References 35 publications

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“…It seems that the emergence of the virus dates back to late summer 2019 in China and that the virus entered the West as early as October 2019 (or earlier?). This is suggested by Li et al [ 17 ] based on mutation analysis. Van Dorp et al [ 18 ] indicate that the disease spread worldwide probably from the beginning of the pandemic.…”

Section: Evolution Of the Virusmentioning

confidence: 99%

Evolution of SARS-CoV-2: Review of Mutations, Role of the Host Immune System

Banoun

2021

Nephron

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Since the reporting of the first cases of coronavirus in China and the publication of the first sequence of SARS-CoV-2 in December 2019, the virus has undergone numerous mutations. In Europe, the spring outbreak (March–April) was followed by a drop in the number of cases and deaths. The disease may have evolved into a milder form. The increase in PCR-positive cases in late summer 2020 did not lead to the expected increase in hospitalizations, ICU admissions, and deaths, based on the severity of the disease in the spring. This difference in disease severity could be due to factors independent of the virus or to the evolution of the virus. This review attempts to identify the mutations that have appeared since the beginning of the pandemic and their role in the temporal evolution of the pandemic. There are a cell and humoral type cross-reactivity in a large part of the population to common cold coronaviruses (HCoVs) and SARS-CoV-2. Evolutionarily important mutations and deletions have emerged in the SARS-CoV-2 genes encoding proteins that interact with the host immune system. In addition, one of the major mutations (in viral polymerase) is logically associated with a higher frequency of mutations throughout the genome. This frequency fluctuates over time and shows a peak at the time when the epidemic was most active. The rate of mutations in proteins involved in the relationship to the immune system continues to increase after the first outbreak. The cross-reactivity on the 1 hand and the viral mutations observed on the other hand could explain the evolution of the pandemic until the summer of 2020, partly due to the evolution of the virus in relation to the host immune system. The immunization campaign began in December 2020: concerns are emerging about a possible escape of the circulating variants vaccines in early 2021. These variants could also escape immunity acquired through infection with the 2020 strains.

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Section: Evolution Of the Virusmentioning

confidence: 99%

Evolution of SARS-CoV-2: Review of Mutations, Role of the Host Immune System

Banoun

2021

Nephron

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“…2). Many points, e.g., (2,3,4), (4,4,7) and (5,8,8) in Fig. 2, have also been recalculated to ensure that they are not repeated or dominated in the k-th layer points to speed up the construction of the MLCS-DAG.…”

Section: Limitations Of Existing Algorithmsmentioning

confidence: 99%

“…For example, for the dominant (1, 2, 4) of the sequences S 1 , S 2 and S 3 (shown in Fig. 4), we can couple the corresponding rows 1-4 of the first, second and third columns from the successor tables ST 1 , ST 2 and ST 3 to obtain all its immediate successors (6, 5, 7), (2,3,8), (3,4,5) and (5,7,6) corresponding to the characters A, C, G, and T, respectively. There is no immediate successor for the dominant (6, 5, 7) due to the coupling results (8, , ), ( , 8, 8), ( , , ) and ( , , ), which indicates none of the points is an immediate successor according to Eq.…”

Section: Successor Tables (St)mentioning

confidence: 99%

Mining Multiple Longest Common Subsequence in Big Sequences

Li¹,

Liu²,

Duan³

et al. 2022

Preprint

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<p> Multiple longest common subsequence (MLCS) mining (a classical NP-hard problem) is an important task in many fields. Numerous applications in these fields can generate very long sequences (i.e., the length of the sequences ≥ 10^4), called big sequences. Such big sequences present a serious challenge to existing MLCS algorithms. Although significant efforts have been made to tackle the challenge, both existing exact and approximate MLCS algorithms fail to deal with big sequences as their problem-solving model MLCS-DAG (Directed Acyclic Graph) is too large to be calculated due to the memory explosion. To bridge the gap, this paper first proposes a new identification and deletion strategy of different classes of non-critical points, which are the points that do not contribute to the solution on the MLCS-DAG. It then proposes a new MLCS problem-solving graph model, called KP-MLCS-DAG (Key Point based MLCS-DAG). A novel parallel MLCS algorithm, called KP-MLCS (Key Point based MLCS), is also presented, which can mine and compress all MLCSs of big sequences effectively and efficiently. Extensive experiments on both synthetic and real-world biological sequences show that the proposed algorithm KP-MLCS drastically outperforms the existing state-of-the-art algorithms in terms of efficiency and effectiveness. The source code of KP-MLCS and related test datasets, etc., can be found online: https://github.com/kp-mlcs/KP-MLCS. </p>

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“…A more enhanced version of the latter approach that utilizes Branch-and-Bound is proposed by Wang et al (2021) in [21]. An approximate algorithm based on the topological sorting strategy was developed and applied to COVID-19 data by Li et al (2020) in [22]. A specific Path Recorder Directed Acyclic Graph (PRDAG) model was designed and used for developing the Path Recorder Algorithm (PRA) by Wei et al (2020) in [23].…”

Section: Introductionmentioning

confidence: 99%

Solving the Longest Common Subsequence Problem Concerning Non-Uniform Distributions of Letters in Input Strings

et al. 2021

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The longest common subsequence (LCS) problem is a prominent NP–hard optimization problem where, given an arbitrary set of input strings, the aim is to find a longest subsequence, which is common to all input strings. This problem has a variety of applications in bioinformatics, molecular biology and file plagiarism checking, among others. All previous approaches from the literature are dedicated to solving LCS instances sampled from uniform or near-to-uniform probability distributions of letters in the input strings. In this paper, we introduce an approach that is able to effectively deal with more general cases, where the occurrence of letters in the input strings follows a non-uniform distribution such as a multinomial distribution. The proposed approach makes use of a time-restricted beam search, guided by a novel heuristic named Gmpsum. This heuristic combines two complementary scoring functions in the form of a convex combination. Furthermore, apart from the close-to-uniform benchmark sets from the related literature, we introduce three new benchmark sets that differ in terms of their statistical properties. One of these sets concerns a case study in the context of text analysis. We provide a comprehensive empirical evaluation in two distinctive settings: (1) short-time execution with fixed beam size in order to evaluate the guidance abilities of the compared search heuristics; and (2) long-time executions with fixed target duration times in order to obtain high-quality solutions. In both settings, the newly proposed approach performs comparably to state-of-the-art techniques in the context of close-to-uniform instances and outperforms state-of-the-art approaches for non-uniform instances.

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COVID-19 Evolves in Human Hosts

Cited by 6 publications

References 35 publications

Evolution of SARS-CoV-2: Review of Mutations, Role of the Host Immune System

Evolution of SARS-CoV-2: Review of Mutations, Role of the Host Immune System

Mining Multiple Longest Common Subsequence in Big Sequences

Solving the Longest Common Subsequence Problem Concerning Non-Uniform Distributions of Letters in Input Strings

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