Results of the 2015 Bioinformatics Research Survey
Results of the 2015 Bioinformatics Research Survey
Posted by Pine BiotechJan 13,2016
What are some bottlenecks you run into?
The non-bioinformatically qualified people making the bioinformatics decisions
Lack of computational training and skills make it difficult to use the tools that are relevant & useful for my research
File format conversion; determining best approach when many are available (requires a lot of reading); genome assembly and other analyses take a lot of time and even with experience will occasionally fail after like a week of running and need to be
data transfer time
Deal with large data volumes
low cpu speed
slow (old) computer
Storage issue, poor documentation
Space issues – for data and for doing analysis in the server
Bandwidth and disk IO
Lack of expertise in the field. Lack of easy-to-use software.
Little staff, little server
Lack of exactness of what is to be done with the data
Intense computing time
Bioinformatics is prediction. Without wet-lab experiments in silico drug design has no scope and wet-lab analysis for few drug design projects are not feasible or not possible to conduct
Storage of data
IT related problems (installation …)
lack of standart data formats
File format conversions
Lack of training (novice).
Visualization, lack of standardized formats, lack of metadata
Drive the project towards biological understanding. And to a lesser extent, bioinformatics handling of computer clusters.
Computing – multithreading
Too many users on the server
Adapting tools built for use on human databases. Converting identifiers between databases in a timely manner.
I need to move beyond simple scripting.
I can find a lot of information on how to do analysis with genomes that are assembled and annotated but little information on the pre-processing steps.
format parsing and installing other people’s software
space and memory
stand alone tools and software, use some biological databases
complexity of the software and unavaiability of required data or computational power
Lack of storage space, computational power.
Due to limited processing resources it is common to encounter freeze situations were you loose time. Furtheremore, it is often no possible to update the software as often as necessary.
Insufficient knowledge in specific topics
memory, storage space
Pipeline integration; documentation
Lab staff expecting miracles from badly designed experiments
lack of good planning from wet lab researchers
Storage Space, computer speed
The vast amount of ram that is required for genome analysis
Finding the right tool for the task (due to the huge number of options).
Server high demand and waiting time.
Mapping miRNA and metabolites to protein coding genes
Not enough samples
Memory, time for file transfer
sequence analysis, searching bibliographic databases etc.
Not enough information from PIs
Memory limitations. Inability to handle large sequences
Downloading software (it’s never as easy as it should be)
Inconsistent metadata format
Hardware limitations on my laptop
Institutional server, expertise/training
unfamiliarity, lack of knowledge
Lack of space
Installing and configuring software
Figuring out what other people’s tools do. Often this means reading the code, but since I only code in Python and JS, sometimes I dont/cant ever know.
Waiting for the sequencing core and/or aligners
What problems running software do you most often encounter with your present solution?
On graphical tools, not enough command line support
Not knowing/understanding syntax rules, file naming rules for existing scripts
lack of proper documentation
with lack of memory
No documentation. Source code is sloppy/unreadable, and No clear guide on how to improve specific types of analyses
Ram of pc
Understanding the process method
most bioinformatic software run on Linux/UNIX
Incompatibility or running into walls during installation
updation of software
installation, format issues, and lack of documentation
Most of the accurately predicting software’s are expensive to use. Even after prediction like drug-like molecules are difficult to test.
Standardization of data and lack of ease in handling huge data
poor documentation and examples
Using poorly documented programms that are quite difficult to use in a modular way
permissions in institutional machines
Softwares suitable to work with polyploid genomes. Solution: Combine softwares or custom scripts.
i dnt hv a specific information
installing dependencies, getting all the input files in the correct format
not supporting scripts
Understanding the output & documentation
low speed cpu
Bugs in code
Unclear input requirements
Obsolete versions and functions, lack of clear documentation
Indent/variable errors for SQL databases
Interconnection with other tools and a lot of headaches trying to solve that
Downloading prerequisite software packages
Clear output. Description incomplete.
Software version in differences
Mutually incompatible versions
Processing time, not actually software related.
Failure to run because of missing software dependencies
online tools are not reliable, sometime they work sometime they don’t work
Documentation is terrible and code lack tests (or test data).
Problem with different linux versions
Limited computational capacity.
limited parameters information
installing softwares in my computer, as well as modify them according to my need.
None. I’m great at using existing tools.
Downloading. I ask others for help
None, though sometimes documentation is lacking
sometimes its hard to understand whats behind the scripting of the programs
installing multiple dependencies
Difficult to install updated versions of software on the cluster.
Poorly documented perl scripts
need more data
Unfamiliarity with the software.
proper corelation between in silico and invivo
slow in processing requests or freezing of screen due to low processor speed
Ability to configure own workflows
many softwares are not compatible with windows
Clear use cases
Differing input/output formats for different programs
documentation of software, software version compatibility with computer OS, software Library dependency
Problems installing software because of dependencies
no problem as such
Unresolved dependency, poor documentation of programme
Lack of documentation; programs that do not compile;
Getting it compiled and working; Having to install same software on multiple systems; Software generally seems to be either too simple/user friendly (very little customization) or very complex with a steep learning curve (10’s of different options
with little documentation); it can be difficult to spot minor errors in programming that result in subtle changes in the results; accessing high-memory machines (512GB+); the vast majority of bioinformatics programs have documentation that simply
does not explain each and every step taken to get a result
software documentation is never explicit
Scripting or programming skills.
Most tools are made for Mac users & don’t run properly on Windows. Also, dependencies.
i dont understand this question
Unclear installation/performing instructions
Lack of adequate documentation, abandonware.
Lack of documentation
big data analytics
Installation. Bad/no documentation
open source software instalation
Not enough compute.
Different results obtains with different tools with same input.
lack of GUI
Documentation, hurdles to learning the software
I dont know what the software does. Like I know what its supposed to do, but I dont know the specifics, and that freaks me out.
If you train students, please say what in your view is the biggest challenge for training students in bioinformatics?
They Lack the basic knowledge about bioinfo
I teach a bioinformatics lab section. The hardest thing is keeping folks interested in the more mundane aspects of learning bioinformatics
Getting them to stop and think about what they’re doing rather than just randomly trying things until they get an answer they like.
fear of programming
Lack of interest in research
teaching biology as well as linux
That they should stay determined about what they want, because they can get lost, due to the very wide branching on bioinformatics which connects with other fields.
lacking of programming training
If they don’t continue using it, they lose it.
My students are ‘wet-lab Biologist’ they look at the computer as a black box
Lack of computing/programming basics
Students fear of linux and CLI
Convincing them that once you get over the “hump” of learning about bioinformatics that it is a lot easier.
thinking about the broad perspectives
Setting their expectations realistically. They view BI as a cure-all solution, not a needs to an end. Most don’t realize that processing the data is merely the start
Be flexible and ever-changing
Establishing the lingo (Sometimes people use the same word to describe 2 different things).
Bioinformatics is a branch where other than coding/programming analysis of biological data also does. But most of the bioinformatics students lack the knowledge of Biology.
Practicing enough to get over the hump.
How diverse you have to be to understand it all
Lack of interest in computer science.
Lack of jobs in non-NGS data analysis. Lack of substantial mathematical background.
For Biology or chemistry background students, Bioinformatics is dry subject, so wont appreciate.
the amount of tools to choose from .
integration of two different fields like computer and biology is very hard task for them
Higher learning curve.
Awareness and confidence
To perform tasks in a clear manner: don’t leave mess and unclear steps on each step of analysis
Lack of software documentation
Scope regarding job
Programming and interpret the result
Exposure to good infrastructure which limits their scope of understanding in bioinformatics
Whole genome analysis and ngs data analysis
Connecting students to the concepts
Understanding good experimental design
lack of CS skills
Finding a research topic interesting enough for the frustration of learning to program.
Lack of updation of databases
Bringing multi-faceted knowledge at one go/step!
Make biologists understand the dry nature of bioinformatics and heuristics and of course, vice versa
Future proofing what they learn
Understanding the bigger context of the problems they are working on
As the field of bioinformatics is very huge and its hard t o impart every required skills to the students.If you teach them how to learn rather than what to do they will learn required skills on thier own
Good, relevant infrastructure.
Getting them to be skeptical about the results of bioinformatics programs
interest and commitment
getting them command-line competent
The lack of computing skills
The variety of backgrounds, everyone has different knowledge gaps that need to be filled in.