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New articles by this author. New citations to this author. New articles related to this author's research. Lastly, SBSA provides a dataset of 4. As outlined in Figure 1 , SBSA integrates two primary inputs with respect to a given reference genome, in order to identify variant-affected binding sequences for a certain type of molecular regulators.
As one primary input to SBSA, a variant can be either a single nucleotide variant or an indel, and can fall into but is not limited to the following categories: somatic mutation, RNA editing event, and SNP. This extended sequence is of the same length as the target motif to be compared to. In such cases, the key is to comparing the derived somatic sequence against the PWM in terms of sequence similarity.
With FIMO, if the binding potential P -value decreases from the reference sequence to the somatic sequence binding propensity increases , we term that the variant causes a Gain of this binding motif; conversely, when the binding potential P -value increases from the reference sequence to the somatic sequence binding propensity decreases , we term that the variant causes a Loss of this binding motif. The new Exact match method uses a simple yet intuitive strategy. Given the PWM of a binding motif, we approve all nucleotides at each individual position that exceed the minimum background probability threshold default: 0.
The pair of reference sequence and somatic sequence are checked against all motif-derived binding sequences. If exact match occurs between a binding sequence and the reference sequence, yet not between a binding sequence and the somatic sequence, a Loss of the binding motif is asserted; conversely, if exact match occurs between a binding sequence and the somatic sequence, yet not between a binding sequence and the reference sequence, a Gain of the binding motif is asserted.
Exact match and personalized genome approaches of SBSA. With the Exact match approach, SBSA derives all likely target sequences blue-arrow-connected paths based on a position-wise probability threshold default at 0. In this example, the reference sequence finds a hit within the group of target sequences whereas the somatic sequence does not, so the Exact match method concludes a Loss of the binding motif in the somatic sequence.
B Illustration of the Personal Genome approach to generating combinatorial somatic sequences. Of note, SBSA applied different strandedness strategies with respect to different types of molecular regulators. SBSA allows an input file containing different types of variants,. The input file can include hundreds or thousands rows truncated to the first 25 rows for certain intensive calculations. By default, these variants are treated mutually independently, leading to somatic sequences that each incorporates a single variant.
Nevertheless, SBSA offers an optional Personalized Genome approach to analyze somatic sequences, where multiple variants in close vicinity are jointly accommodated in one somatic sequence Figure 2B. Indels are considered as well as point variants. With the Personalized Genome approach, SBSA derives 2 k — 1 somatic sequences for k adjacent variants, with each representing one combination of these adjacent mutations.
After enumerating all possible somatic sequences, Gain or Loss of the target motif is inferred by comparing the reference sequence against the group of somatic sequences. As the term implies, Personalized Genome approach attempts to accommodate multiple adjacent variants manifested in an individualized genome, so it is only valid when the input variant file is summarized from a single subject rather than from a cohort. To validate the gain-of-function phenotype of the mutations in the TERT gene promoter, we used luciferase reporter plasmids i.
Transfected cells were incubated for 24 h and the luciferase activity normalized by renilla was measured using the Dual-Luciferase Reporter assay system Promega. Experiments were repeated three times independently, with three technical repeats in each experiment. Research on somatic mutations in binding sequences has been accelerated since high-throughput sequencing technology became available.
To promote the identification of functional variants in these binding sequences, we developed SBSA to enable fast and easy detection of somatic binding sequences at the genome-scale. The primary input to SBSA is a file containing numerous genomic variants.
This variant file can use either a standard Variant Call Format v4. Along with the variant file, the user needs to inform SBSA on the species of the investigated biological sample.
In the background, reference genomes for 26 species are pre-installed. Users must make sure the chromosome names in the variant file are of the same form as those of the background reference genome file a list of standard chromosome names of all pre-installed genomes is provided as a reference.
SBSA combines the variant specification and the reference genome to derive a pair of somatic sequence and reference sequence, which carries and lacks the specified variant, respectively. The other input to SBSA defines the concerned binding target s. This input can be provided by the user ab initio , or be chosen from built-in libraries.
By design, inputting a genomic interval invalidates a sequence similarity search, so SBSA does not invoke either FIMO or exact match in this scenario; instead, it seeks any overlapping between the variant-derived somatic sequences and the target genomic intervals, and annotates overlaid miRNA seeds if there are any. All such auxiliary annotation information can help guide a validity or creditability based prioritization of the immense annotation results.
The primary output from SBSA analysis takes the form of a comma-separated spreadsheet, where each row represents one somatic sequence of potential regulator-binding disruption. To guard against a prolonged standby waiting time, we allow the user to leave an email address to receive a download link to the analysis result.
Because SBSA performs diverse annotation modules for diverse types of input Figure 1 , which might entail distinct sets of parameters, the input files and advanced parameters are fed in a step-by-step gradient, and dynamic inactivation of irrelevant parameters is rendered based on the inputs at prior steps. To demonstrate major application contexts and output templates, we provide a few input examples with discrete sets of pre-populated inputs and parameters, so that demonstrative analyses can be readily exerted and representative results can be generated momentarily.
Lastly, we rendered a comprehensive documentation that provides a detailed manual of all inputs and outputs. Many tools and studies have been dedicated to analyzing binding sequences of TFs. However, they are not purported for straightforward annotation. The output of FIMO is the location of likely binding sites within the input sequences. Jayaram et al. For the transcription factor binding site prediction tools, the input is a ChIP-seq-derived PWM, and the output consists of the candidate transcription factor binding sites.
Lee et al. Similarly, another tool GERV 47 evaluates the effect of regulatory variants for transcription factor binding, tackling input of ChiP-seq data.
In yet another work, Reshef et al. It does not enable novel discovery. RSAT is a multi-function genome analysis suite; when its two specific functions are performed sequentially, the user can achieve annotation of variants with respect to motifs.
NA not applicable indicates the tool is not equipped with the referred functionality. For example, FIMO aims to compute the binding potential between a sequence and a motif, and it is not populated with a TF database. X sign denotes that the functionality is not supported. The runtime of SBSA can range from seconds to minutes, and it scales with two parameters: number of variants and number of binding sequences. Due to the potential long runtime, users can choose to be notified of the result download link via email.
To demonstrate SBSA, we conducted analyses with the following five datasets accessible on the SBSA website : i somatic mutation data from 10 subjects of 33 cancer types from TCGA, consisting of 33 variant files; ii somatic mutation data from 19 subjects of 57 cancer types from 81 projects within The ICGC, consisting of 81 variant files; iii 4.
Thorough analyses of these data using SBSA identified 1 consequential somatic binding sequences. All these identified mutations have been curated into our companion database SMDB 15 and can be queried and downloaded freely.
Our annotation identified many known somatic binding sequences and many high-frequency novel results. The function of this mutation in driving TERT expression has been studied in human cancer cell lines 6 , 12 , We also conducted our own luciferase reporter experiment in melanoma cells to further validate the functional impact of this TERT mutation.
This observation is consistent with previously published data 14 and suggests that the gain of an ETS binding site in the mutant promoter activates TERT gene expression. High-frequency examples from SBSA demonstrative analysis. Blue and red nucleotide strings denote the non-altered reference sequences and the altered somatic sequences, respectively.
This particular RNA editing event is ubiquitously observed in human cancers. ESRP2 is another cancer-related RBP 50 with known functionality such as suppressing cell motility in head and neck carcinoma cell lines 52 and driving alternative splicing patterns in prostate cancer This mutation causes an altered binding sequence for miRp Figure 3F.
The biological effects of such mRNA target alterations have been demonstrated previously 9 , Identification of binding motifs for transcriptional regulators has been a fundamentally important topic in molecular biology, and for this sake numerous computational algorithms have been continually developed in the past several decades. The abundant algorithms were based on a variety of statistical frameworks, including dynamic programming 34 , hidden Markov chain 54 , deep learning 55 , etc. In reflecting on the origin of his PhD topic, Duan Bin mentioned being inspired by the works of Prof.
Ezra Vogel, a renowned Harvard social scientist. How did the end of the Cold War affect bilateral relations? On the border issue, how did the border crisis management and control between China and India come into being, and what are the binding forces?
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