Analyze datasets and surveys of any size with unmatched performance and a modern, easy-to-use interface that does not require any programming knowledge.
Create publication-ready heat maps, venn diagrams, volcano plots, and more in milliseconds with just one workspace file.
Make your discovery with our seamless and integrated analysis features such as Most Changed, Ontology, and Principal Component Analysis (PCA).
Search Suggestions, Automatic Test Groups and Batch Export
are just a few of the
features you won’t find anywhere else.
Create charts for simple datasets instantly, and analyze large datasets faster than ever before, on any Mac. Stars leverages the power and efficiency of Apple Silicon to deliver the fastest possible experience (Intel Macs are also supported).
Make your Stars workspace in a matter of minutes (no consolidation of data files required), set up your test groups and perform a search. Don’t know what groups to create? Stars can automatically suggest and create groups for any metadata attribute, and for more complex analysis, you can define your own, versatile groups.
We created Stars & QVey to make analysis fast, flexible and fun.
Transform raw complexities into actionable insights.
Aseesa derived from the Sanskrit root word Asesa (अशेष) has several meanings:
- Complete; (in every detail)
- All encompassing; (of everything)
- Without a remainder;
- Unlimited;
- Of all;
At Aseesa, we understand that biology is integrated and all encompassing, it is unlimited, complete,
and for
All, like Aseesa.
Aseesa was founded with the ambitious goal of making complex data analysis simple. While the look and feel of consumer-oriented apps has improved dramatically over the past decade, professional and academic software has evolved much more slowly. Some of the best analysis tools—used by millions worldwide—still lack an intuitive user interface, and many are simply code packages that require programming. Therefore, we created Stars and Qvey with a vision to wrap enterprise-grade analysis tools in an interface that rivals the best consumer-oriented apps.
To make scientific discovery efficient, fast, precise, and transparent without compromising on quality or reproducibility. The goal is to present a truly groundbreaking approach to use various statistical and computational methods to interpret data of various types, identify trends, and draw conclusions. The insights gained from an unbiased analysis will help scientists understand fundamental principles, prove, or disprove hypotheses, and contribute to the advancement of scientific knowledge. Working in silos has led to overlooking of several adverse/side events for a given drug/treatment that are discovered after being deployed population-wide, sometimes in healthy populations. Thus, a systems and integrated approach for data analysis has been the bottle neck and despite outstanding technological advances, effective treatments and cures have been elusive.
Our vision is to assimilate all meanings of Aśeṣa to present the scientific community with a complete picture of their data. The vision is to unravel data from complex biological systems to generate integrated and precise information that is foundational. Basic science data grounded in facts is the foundation for making informed decisions, publishing research findings, and paving the way for future discoveries. Data analysis is pivotal in accelerating the translation of scientific discoveries into tangible benefits for individuals and society, ultimately fostering advancements in medicine and healthcare.
In addition to all the features provided by Stars, our expert analysts will design
customized
algorithms and workflows to gain insights from your datasets. This flexible and accurate
approach is valuable when dealing with large-scale or unique data, empowering you to make
data-driven decisions and uncover hidden patterns. Omics plays a pivotal role in deciphering
complex biochemical networks, biomarker discovery, and advancing personalized medicine by
integrating genomics, proteomics, transcriptomics, and metabolomic data.
Clinical data analysis is the systematic examination of patient-related information for medical research, patient care, or clinical trials. It involves interpreting data from electronic health records, lab tests, and imaging to find patterns, and correlations. Making informed decisions improves patient outcomes and advances medical research. It's essential for evidence-based medicine, personalized treatments, and regulatory compliance; safeguarding patient privacy is key. Through advanced computational techniques, researchers analyze data to identify potential therapeutic targets, assess treatment efficacy, and optimize patient outcomes.
Basic science includes analyzing experimental data obtained from laboratory studies and research in the fields of biology, chemistry, physics, and other scientific disciplines. Researchers use various statistical and computational methods to interpret data, identify trends, and draw meaningful conclusions. The insights gained from this analysis help scientists understand fundamental principles, validate hypotheses, and contribute to the advancement of scientific knowledge. Effective basic science data analysis is crucial for making informed decisions, publishing research findings, and paving the way for future discoveries.
Translational science involves the systematic examination and interpretation of experimental findings, clinical data, and other relevant information to bridge the gap between basic scientific research and practical applications in healthcare. This interdisciplinary approach aims to translate laboratory discoveries into innovative medical treatments, diagnostic tools, and interventions for patients. Translational science data analysis plays a pivotal role in accelerating the translation of scientific discoveries into tangible benefits for individuals and society, ultimately fostering advancements in medicine and healthcare.
Transcriptomics encompasses the study of the structure and spatio-temporal function of the entirety of all types of RNA molecules found within an organism. To profile genome-wide gene expression levels in a specific cell-type or to interrogate a defined set of mRNAs for genes at a time, various sequencing approaches such as the next-generation sequencing (NGS) technology or RNA sequencing (RNA-Seq) are used. By comparing transcriptomes of different types of cells (for e.g., neuron versus glial cells), a deeper and contextual understanding of how that specific cell types works in different situations/conditions can be obtained.
Next-generation sequencing (NGS) is a revolutionary genomics technology that enables rapid
and cost-effective analysis of DNA and RNA. Unlike traditional methods, NGS can
simultaneously sequence millions of DNA fragments in parallel. This high-throughput approach
has diverse applications in cancer research, personalized medicine, and evolutionary
studies, providing valuable insights into genetic variations and disease associations. NGS
has transformed the field of genomics by generating vast amounts of data, which, when
analyzed with computational tools, enhances our understanding of biological processes and
diseases.
16S sequencing is a specialized molecular biology technique used to analyze and identify the bacterial and archaeal communities present in a sample. It targets the 16S rRNA genes, which is highly conserved across these microorganisms. By amplifying and sequencing 16S rRNA, researchers can classify and quantify the diversity of bacteria and archaea in various environments, such as soil, water, the human gut, and more. 16S sequencing has significantly advanced a deeper understanding of the complex interactions between microorganisms and their environments and helped understand the roles of microorganisms in diverse fields.
Proteomics is a cutting-edge field of molecular biology that focuses on the comprehensive study of proteins in a biological system. It involves the identification, quantification, and characterization of all proteins present in a cell, tissue, or organism. Through advanced technologies like mass spectrometry and protein microarrays, proteomics allows researchers to explore protein functions, interactions, modifications, and expression patterns. By unraveling the complex protein networks and pathways, proteomics contributes significantly to our understanding of various biological processes, disease mechanisms, and drug development.
Metabolomics is an emerging discipline in the field of omics that focuses on the
comprehensive analysis of small molecules, known as metabolites, in biological systems. It
aims to understand the metabolic processes and pathways within cells, tissues, or organisms,
providing valuable insights into their physiological state and responses to internal and
external stimuli. Advanced analytical techniques, such as mass spectrometry and nuclear
magnetic resonance spectroscopy, are used to identify and quantify metabolites, enabling
researchers to study the dynamic changes in metabolite profiles under different
conditions.
RNA sequencing (RNA-seq) is a high-throughput technique used in molecular biology and/or
transcriptomics to measure the expression levels of all RNA molecules in a biological
sample. By converting RNA into complementary DNA (cDNA) and then sequencing it, researchers
can gain valuable insights into gene expression, alternative splicing, and various
biological processes associated with functions of coding and non-coding RNA molecules.
RNA-seq has revolutionized genomics research and is instrumental in understanding gene
regulation, cell differentiation, and disease mechanisms.
