Tadpole software: Tadpoles -Daycare Operations, Billing & Classroom Management App

Tadpoles -Daycare Operations, Billing & Classroom Management App

All-in-One Childcare Center App Built by and for Early Childhood Educators

Optimize and streamline your center daily operations

View child and teacher locations at a glance, track classroom ratios in real time, send important notifications to classrooms or families, and easily create and manage customized billing and payment plans. Minimize the time and effort needed to complete everyday administrative tasks.

Simplify daily tasks with an intuitive, easy-to-use interface

Make communication with families seamless, efficient, and paperless. Teachers can easily design detailed plans with co-teachers, create daily reports for each child, and save time with streamlined attendance and transitions tracking. Tadpoles is easy for everyone, from novice to tech-savvy teachers.

Connect classroom management and formative assessment

Seamless integration between Tadpoles and GOLD empowers teachers to easily gather formative assessment data from the same app they’re using for classroom management and family communication, creating more time to engage children in meaningful learning.

Explore Our Solutions

Build Bridges Between School and Home with Tadpoles

Explore Demo

Keep families engaged and connected

Provide families with important updates about their children’s day with a daily report that securely shares photos, videos, and up-to-the-minute information about their children’s routines. Two-way communication enables instant messaging between school and home for families, teachers, and administrators.

Watch the Video

Keep children, families, and staff healthy and safe

Enable families and teachers to work together to provide meaningful education and care without compromising health and safety. With two-way communications and health screening features that satisfy state, local, and center requirements and provide peace of mind to staff and families, you can capture symptoms of concern early and often.

Manage, track, and grow enrollment

Easily manage and track your enrollment pipeline, identify potential gaps in each classroom, and determine appropriate actions with easy-to-use enrollment forecasting tools.

We have enjoyed using Tadpoles. Our teachers, administrators, and parents love the ease of use and the ability to communicate so effectively. The administrative dashboard is one of our favorite features; it allows us to effortlessly track student and teacher movements as well as maintain ratios and quickly access student information. Tadpoles has simplified classroom procedures and increased our teachers’ ability to provide detailed information to parents.

Jenny Jackson

Marketing Coordinator, Brighton Academy, The Woodlands

More Reasons to Love Tadpoles

Built for the Specific Needs of Early Childhood

Tadpoles is built by and for early childhood educators and integrates with the only ecosystem designed exclusively for early childhood.

Easy to Implement

Intuitive and simple to use, Tadpoles is just as easy for novice technology users as it is for tech-savvy teachers—it’s easy for everyone.

Teaching Strategies is the #1 Provider of Early Childhood Solutions

Tadpoles connects over 430,000 families and serves nearly 500,000 children every day.

Featured Resources

Related Products

Streamline Operations and Engage Families

Let us help you begin your journey with Tadpoles: it’s everything you need to build strong relationships with families, streamline center management, and simplify teachers’ day-to-day tasks.

Start Your Free Trial

Tadpole Guide – DOE Joint Genome Institute

Tadpole is a kmer-based assembler, with additional capabilities of error-correcting and extending reads. It does not do any complicated graph analysis or scaffolding, and therefore, is not particularly good for diploid organisms. However, compared to most other assemblers, it is incredibly fast, has a very low misassembly rate, and is very adept at handling extremely irregular or super high coverage distributions. It does not have any annoying side-effects of generating temp files and directories. Also, it can selectively assemble a coverage ‘band’ from a dataset (for example, just areas with a depth between 1000x and 1500x). These features make it a good choice for microbial single-cell data, viruses, organelles, and preliminary assemblies for use in binning, quality recalibration, insert-size estimation, and so forth. Tadpole has no upper limit on kmer length.

Tadpole’s parameters are described in its shell script (tadpole.sh). This file provides usage examples of various common tasks.

*Notes*

Memory:

Tadpole will, by default, attempt to claim all available memory. It uses approximately 20 bytes per unique kmer for k=1-31, 30 bytes per kmer for k=32-62, and so forth in increments of 31. However, with most datasets, the bulk of the kmers (and thus memory) are unwanted error kmers rather than genomic kmers. It is possible to save memory by making Tadpole ignore low-quality kmers using the “minprob” flag (this ignores kmers that, based on their quality scores, have less than a specified probability of being error-free). Alternatively, bloom filters can be used to screen low-depth kmers efficiently using the “prefilter” flag. Also, memory will be used somewhat more efficiently if the “prealloc” flag is applied, which makes Tadpole allocate all physical memory immediately rather than growing as needed. If Tadpole runs out of memory on a dataset despite using these options, you may consider using BBNorm to normalize or error-correct the data first. Both of those will reduce the number of unique kmers in the dataset.

Processing modes and output streams:

The default mode is contig-building; reads are processed, kmers are counted, then contigs are made from kmers and written to a file. The alternate mode is error correction / extension, which can be entered with the flag “mode=correct” or “mode=extend”; either of those modes supports both error-correction and extension (making the reads longer by assembling at their ends). In contig mode, the reads will be processed once, and the contigs will be written to “out”. In correct or extend mode, the reads will be processed twice (once to count kmers, and once to modify the reads), and will be written to “out”.

Threads:

Tadpole is fully multithreaded, both for kmer-counting and for the output phase (contig-building, error-correction, or extension). You should allow it to use all available processors except when operating on a shared node, in which case you may need to cap the number of threads with the “t” flag.

Kmer Length:

Tadpole supports unlimited kmer length, but it does not support all kmer lengths. Specifically, it supports every value of K from 1-31, every multiple of 2 from 32-62 (meaning 32, 34, 36, etc), every multiple of 3 from 63-93, and so forth. There is a wrapper script, tadwrapper.sh, that will assemble a range of different kmer lengths to determine which is best. Typically, about 2/3rds of read length is a good value for K for assembly. For error-correction, about 1/3rd of read length is better. In order to assemble with longer kmers, it is possible to error-correct and extend reads with short kmers (such as 31-mers), then use the longer extended (and potentially merged) reads to assemble with a longer kmer. Longer kmers are better able to resolve repetitive features in genomes, and thus tend to yield more continuous assemblies. The tradeoff is that longer kmers have lower coverage.

Shave and Rinse:

These flags examine the graph immediately after kmer-counting is finished, to remove kmers that cause error-induced branches. Specifically, “shave” removes kmers along dead-end paths with very low depth that branch off from a higher-depth path, and “rinse” removes kmers along very-low-depth bubbles that start and end at branches off a higher-depth path. Both are optional and can be applied to any processing mode. They do not currently seem to make a significant difference.

Continuity and fragmentation:

Tadpole is designed to be conservative and avoid misassemblies in repetitive regions. As a result, the assemblies may sometimes be more fragmented than necessary. With sufficient coverage and read length, fragmentation can often be reduced by choosing a longer kmer. Alternately, reducing the value of branchmult1 and branchmult2 (to, say, “bm1=8 bm2=2”) can often increase the continuity of an assembly, though that does come with an increased risk of misassemblies.

*Usage Examples*

Assembly:

tadpole.sh in=reads.fq out=contigs.fa k=93

This will assemble the reads into contigs. Each contig will consist of unique kmers, so contigs will not overlap by more than K-1 bases. Contigs end when there is a branch or dead-end in the kmer graph. The specific triggers for detecting a branch or dead-end are controlled by the flags mincountextend, branchmult1, branchmult2, and branchlower. Contigs will only be assembled starting with kmers with depth at least mincountseed, and contigs shorter than mincontig or with average coverage lower than mincoverage will be discarded.

Error correction:

tadpole.sh in=reads.fq out=ecc.fq mode=correct k=50

This corrects the reads and outputs corrected reads. Correction is handled by two algorithms, “pincer” and “tail”. Pincer corrects errors bidirectionally, using kmers on the left and right; therefore, it can only work on bases in the middle of the read, at least K away from either end. Tail is not as robust, but is able to work on the ends of the read. So, it’s best to leave them both enabled, in which case the middle bases are corrected with pincer, and the end bases are corrected with tail.

Error marking:

tadpole.sh in=reads.fq out=ecc.fq mode=correct k=50 ecc=f mbb=2

This will not correct bases, but simply mark bases that appear to be errors by replacing them with N. A base is considered a probable error (in this mode) if it is fully covered by kmers with depth below the value (in this case, 2). Mbb and ecc can be used together.
Read Extension:
tadpole.sh in=reads.fq out=extended.fq mode=extend k=93 el=50 er=50

This will extend reads by up to 50bp to the left and 50bp to the right. Extension will stop prematurely if a branch or dead-end is encountered. Read extension and error-correction may be done at the same time, but that’s not always ideal, as they may have different optimal values of K. Error-correction should use kmers shorter than 1/2 read length at the longest; otherwise, the middle of the read can’t get corrected.

Scientists have created a digital twin of a tadpole : luckyea77 — LiveJournal

A Russian-British team of scientists has developed a digital twin of a tadpole, in which both the structure and functions of the nervous system of this vertebrate organism and the structure of its body are recreated. A detailed biomechanical 3D model controlled by a digital brain interacts with a virtual physical environment, allows you to observe the behavior of an object and provides unique opportunities for neuroscience research. The article was published in the journal PLOS Computational Biology.

General view of the tadpole model in the process of swimming with a graph of the activity of the main control neurons and visualization of fluid velocities

There are at least two main ways to an artificial intelligence that is not inferior to the human one and is aware of itself, there are at least two main ways: invent it yourself or copy it from nature. The first path has not yet led to the desired goal, but it has caused the rapid development of computer technology and various areas in the field of artificial intelligence, from playing chess and expert systems to humanoid robots. Many developments have firmly entered the daily life of people in the form of software for various gadgets and are able to communicate with a person in natural language.

The second way is to investigate and reproduce the work of a real biological brain, if this turns out to be fundamentally possible. By the way, in humans, it consists, according to estimates, of about 86 billion neurons. As the main goal of the most complex and ambitious project in this area, the Human Brain Project (the “Human Brain” project), which started in 2013, was planned to digitize and model the human brain at the cellular level in ten years, and as a training task and an intermediate result do the same for the rat brain (200 million neurons). However, the scientific problem posed turned out to be much more difficult than expected, and so far only a small part of the brain functions as a model, so it is very difficult to determine whether it works correctly. Another international project, OpenWorm, started in 2011, was aimed at finding out whether it is possible in principle to recreate the structure and functions of the nervous system of a whole living being so well that the virtual organism behaves like a real one. One of the simplest multicellular organisms, the microscopic worm Caenorhabditis elegans, which has only 302 neurons, was chosen as the object of modeling. A significant contribution to the OpenWorm project was made by a research group led by Doctor of Physical and Mathematical Sciences Andrey Yuryevich Palyanov from the Institute of Informatics Systems. A.P. Ershov SB RAS, who started working on this problem back in 2009year.

The mentioned projects correspond to two extreme points on the scale of the mind – from the simplest to the extremely complex, human, which has not yet been fully understood or modeled. What is the golden mean, which will allow us to achieve significant results today? In the brain of even the simplest vertebrate organisms, there are more than four million neurons, modeling of which also does not seem to be such an easy task. However, it is not at all necessary that the object of study and modeling be an adult organism. A very good choice seems to be the Xenopus tadpole, which has been studied by professor of zoology at the University of Bristol Alan M. Roberts and colleagues for several decades. The brain of an adult frog has more than 16 million neurons, while a two-day-old tadpole has only a few thousand, but with each passing day their number grows. The capabilities of the sensory system in the early days are rather limited – mainly mechanosensory and the ability to perceive light, however, even at this stage of development, the tadpole is able to respond to external influences and avoid potential dangers. But to model this, the nervous system alone is not enough: the body needs a virtual body and an environment with valid physical laws.

Digital twin of a tadpole in action – swimming caused by touch A.P. Ershov SB RAS, Head of the Laboratory of System Dynamics A.Yu. Palyanov and Chief Researcher of the Laboratory of Neural Networks of the Institute of Mathematical Problems of Biology of the Russian Academy of Sciences, Doctor of Physical and Mathematical Sciences Roman Matveyevich Borisyuk, this problem was successfully solved. To do this, Andrei Palyanov created a specialized software system Sibernetic-VT and, on its basis, developed a biomechanical model of the body of a tadpole that interacts with a virtual three-dimensional environment, in this case, with the water in which it swims. This makes it possible, on the one hand, to supply the nervous system with sensory signals, and, on the other hand, to observe the results of its work, which are also expressed in the behavior of the object. “The created model, which combines the “brain” and “body” of a tadpole, is also capable of demonstrating the reaction of its digital twin to touch, which in nature is a signal of potential danger, the proximity of a predator (therefore, it is necessary to immediately swim away), says Andrey Palyanov. “At this stage of development, there are more than 2,300 neurons in the nervous system, represented by twelve main types.”