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Softberry Inc.

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Mount Kisco, New York, US

About Softberry Inc.

Softberry works in close contact with its clients and collaborators to meet all their computational genomics needs. Our team of researchers and software developers is able to solve most complex problems related to our area of expertise.

**Recent publications in in Science and Nature that used Softberry... Show more »

Softberry works in close contact with its clients and collaborators to meet all their computational genomics needs. Our team of researchers and software developers is able to solve most complex problems related to our area of expertise.

Recent publications in in Science and Nature that used Softberry software :

  • Bailey, et al., (2014) Adaptive synonymous mutations in an experimentally evolved Pseudomonas fluorescens population. Nature Communications 5, 4076.
  • Dijkstra, et al., (2014) TH2 and Treg candidate genes in elephant shark. Nature 511 (7508), E7-E9.
  • Moroz, et al., (2014) The ctenophore genome and the evolutionary origins of neural systems. Nature 510, 109-114.
  • Lienard, et al., (2014) Sex pheromone biosynthetic pathways are conserved between moths and the butterfly Bicyclus anynana. Nature Communications 5, 3957.
  • Tang, et al., (2014) Genome of the human hookworm Necator americanus. Nature Genetics 46, 261-269.
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Our Services (23)

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DNA Sequencing Data Analysis

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SNP Discovery Sequencing

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Cloud Computing

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RNA-Seq Data Analysis

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Whole Genome Assembly

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NGS Data Analysis

Next generation sequencing data analysis
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Metagenomics Data Analysis

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Genome Annotation

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We provide custom genome annotation services using our unique set of genome analysis tools. Typical annotation process involves the following steps:

  • Train parameters of gene prediction programs on known genes of given organisms
  • Whenever practical, learning set is expanded to include genes of related organisms
  • Train and... Show more »

We provide custom genome annotation services using our unique set of genome analysis tools. Typical annotation process involves the following steps:

  • Train parameters of gene prediction programs on known genes of given organisms
  • Whenever practical, learning set is expanded to include genes of related organisms
  • Train and test parameters and select those optimal for the organism under analysis
  • Predict genes
  • Find exons similar to known proteins and ESTs and use this information to improve accuracy of ab initio gene prediction
  • Use our genome comparison tool to find conserved regulatory regions and further improve gene identification using sequences of related organisms
  • Determine secondary structure, functional site signature and cell location of predicted proteins.
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Bioinformatics

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Sequencing Data Analysis and Management

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Nucleic Acid Services

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Nucleic Acid Data Analysis

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Data Services

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Computer Science, Software Development, and IT Services

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DNA Sequencing

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Equipment

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Biostatistics & Bioinformatics

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Computational Modeling

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Biology

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DNA Services

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Next Generation Sequencing (NGS)

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Nucleic Acid Analysis

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Biochemistry & Molecular Biology

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Victor Solovyev

Chief Scientific Officer

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